{ "name": "ML Systems Taxonomy", "version": "0.2-draft", "status": "editorial-draft", "url": "https://mlsysbook.ai/explore/", "license": { "name": "CC BY-NC-SA 4.0", "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/" }, "citation": "Vijay Janapa Reddi. ML Systems Taxonomy, version 0.2-draft. Machine Learning Systems, 2026. https://mlsysbook.ai/explore/", "note": "Editorial draft for expert review; not a claim of completeness or consensus.", "tagging_rules": [ "Tag an item with the concepts it centrally teaches or tests: prefer one, and add a second or third only when the item cannot be answered without it. Do not tag prerequisites or passing mentions.", "Tag a subject alone when the item covers the subject broadly or no concept is specific enough. A subject tag is a valid, complete tag.", "When no concept fits, tag the closest subject and record the missing idea as a proposed concept; proposals feed the next version.", "Store concept identities (mlsys.kv-cache) and subject identities, not codes; codes can be renumbered between versions and identities cannot.", "When a concept seems to belong to two subjects, use its home and follow the home rule and its worked examples; related subjects are navigation, not alternative tags.", "Do not tag a workload-class concept (Large language model, Transformer) only because the item involves one; tag it when the item is about that workload class.", "Tag the mechanism, not the platform. Cloud, edge, mobile, and TinyML are contexts. Use Edge & embedded hardware or On-device deployment as the subject only when the item is about the device class itself or about converting, packaging, or updating models for it, not when a device is merely where a memory, interconnect, or scheduling question arises.", "Tag a calculation by what it is about. An estimate of KV-cache size is about the KV cache. Use Performance models, Workload characterization, or Profiling as the subject only when the item teaches the modeling or measurement method itself.", "When an item asks for the effect of a technique (the energy saved by INT8, the throughput gained by batching), the technique is the subject and the affected quantity may be a second concept.", "Depth is recorded separately (for example, StaffML levels L1 to L6+). A competency is a concept or subject paired with a depth.", "When a concept is retired, its identity maps to the concept named in replaced_by. When a concept is split, the old identity is retired and existing tags are re-reviewed; until then they resolve to the replacement." ], "placement_examples": [ { "concept": "mlsys.kv-cache", "home": "llm-inference", "why": "Implements generative serving; it exists for speed but is part of how decoding works." }, { "concept": "mlsys.kv-cache-quantization", "home": "model-compression", "why": "Its only purpose is efficiency, and the same technique applies to weights and activations across layers." }, { "concept": "mlsys.speculative-decoding", "home": "llm-inference", "why": "Changes how the serving stage generates tokens." }, { "concept": "mlsys.activation-recompute", "home": "training-memory", "why": "Implements a training-stage memory plan." }, { "concept": "mlsys.carbon-aware-scheduling", "home": "energy-carbon", "why": "Its only purpose is lower carbon, and it applies to training, batch inference, and data jobs alike." }, { "concept": "mlsys.fusion", "home": "ml-compilers", "why": "Implements the compiler layer." }, { "concept": "mlsys.dp-sgd", "home": "privacy-dp", "why": "Its only purpose is privacy." } ], "groups": [ { "code": "F", "id": "foundations", "label": "Foundations", "question": "What does the system compute, what must it achieve, and how do we reason about its cost?", "areas": [ { "code": "F1", "id": "workloads-requirements", "label": "Workloads & requirements", "question": "What must the system achieve, for which workload, and how is success decided?", "scope": "Targets, workload descriptions, and objectives that every later engineering choice is judged against.", "subjects": [ { "code": "F1.1", "id": "requirements-slos", "label": "Requirements, SLOs & baselines", "definition": "Stating measurable targets for a system's behavior and service, and the baselines and budgets against which they are judged.", "includes": [ "Acceptance criteria and baselines", "SLIs, SLOs, error budgets", "Latency budgets across a request path" ], "excludes": [ "Measured latency behavior and goodput ([[latency-goodput]])", "Statistical evaluation of model quality ([[evaluation]])" ], "contexts": [ "interactive", "batch" ], "concepts": [ "mlsys.requirements", "mlsys.baseline", "mlsys.slo", "mlsys.sli", "mlsys.error-budget", "mlsys.latency-budget" ], "related_concepts": [ "mlsys.tail-latency", "mlsys.slo-goodput" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Lifecycle", "url": "https://mlsysbook.ai/vol1/03_ml_lifecycle/03_ml_lifecycle.html" }, { "volume": "vol1", "chapter": "Benchmarking", "url": "https://mlsysbook.ai/vol1/12_benchmarking/12_benchmarking.html" }, { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" } ] }, { "code": "F1.2", "id": "workload-characterization", "label": "Workload characterization", "definition": "Describing a workload's compute, memory, data, and traffic demands, and where it runs, so it can be engineered and provisioned for.", "includes": [ "FLOP, byte, and memory accounting", "Deployment tiers from cloud to TinyML", "Workload traces" ], "excludes": [ "Node-level job placement ([[cluster-scheduling]])", "Measured profiles ([[profiling-utilization]])" ], "contexts": [ "edge", "distributed", "deep-learning" ], "concepts": [ "mlsys.workload", "mlsys.placement", "mlsys.flop-accounting", "mlsys.memory-footprint", "mlsys.workload-trace" ], "related_concepts": [ "mlsys.arrival-rate", "mlsys.capacity-planning" ], "taught_in": [ { "volume": "vol1", "chapter": "System Paradigms", "url": "https://mlsysbook.ai/vol1/02_system_paradigms/02_system_paradigms.html" }, { "volume": "vol1", "chapter": "Neural Computation", "url": "https://mlsysbook.ai/vol1/05_nn_computation/05_nn_computation.html" }, { "volume": "vol1", "chapter": "Network Architectures", "url": "https://mlsysbook.ai/vol1/06_nn_architectures/06_nn_architectures.html" }, { "volume": "vol2", "chapter": "Introduction", "url": "https://mlsysbook.ai/vol2/01_introduction/01_introduction.html" } ] }, { "code": "F1.3", "id": "objectives-decisions", "label": "Objectives, proxies & decision policies", "definition": "How learned outputs become decisions, how optimized proxies relate to intended outcomes, and how competing objectives are traded.", "includes": [ "Proxy objectives and reward hacking", "Cost-sensitive thresholds", "Multi-objective tradeoffs" ], "excludes": [ "Fairness criteria ([[responsible-ai]])", "Optimizer and loss mechanics ([[training]])" ], "contexts": [ "classical-ml", "agentic" ], "concepts": [ "mlsys.proxy-objective", "mlsys.decision-policy", "mlsys.cost-sensitive-decision", "mlsys.pareto-frontier" ], "related_concepts": [ "mlsys.calibration", "mlsys.selective-prediction", "mlsys.reward-hacking" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Lifecycle", "url": "https://mlsysbook.ai/vol1/03_ml_lifecycle/03_ml_lifecycle.html" }, { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol3", "chapter": "Reinforcement Learning from Verifiable Rewards", "url": "https://mlsysbook.ai/vol3/17_rlvr/17_rlvr.html" } ] } ] }, { "code": "F2", "id": "models-architectures", "label": "Models & architectures", "question": "What does the model compute, and how does its structure set system cost?", "scope": "Model families, building blocks, and architectural choices viewed as workloads, plus the artifacts that carry them.", "subjects": [ { "code": "F2.1", "id": "model-families", "label": "Model families", "definition": "The classical and neural model regimes, with their inductive assumptions, viewed as workloads with characteristic compute and memory profiles.", "includes": [ "Classical models and neural networks", "Transformers, recommendation models, diffusion models" ], "excludes": [ "Learning paradigms ([[training]])", "Layer-level building blocks ([[architecture-blocks]])" ], "contexts": [ "classical-ml", "deep-learning", "generative", "recommendation", "multimodal" ], "concepts": [ "mlsys.linear-model", "mlsys.tree-ensemble", "mlsys.kernel-method", "mlsys.nearest-neighbor", "mlsys.neural-network", "mlsys.inductive-bias", "mlsys.transformer", "mlsys.recommendation-model", "mlsys.diffusion-model", "mlsys.multimodal-model", "mlsys.gan", "mlsys.graph-neural-network", "mlsys.large-language-model", "mlsys.transformer-variants" ], "related_concepts": [], "taught_in": [ { "volume": "vol1", "chapter": "Neural Computation", "url": "https://mlsysbook.ai/vol1/05_nn_computation/05_nn_computation.html" }, { "volume": "vol1", "chapter": "Network Architectures", "url": "https://mlsysbook.ai/vol1/06_nn_architectures/06_nn_architectures.html" } ] }, { "code": "F2.2", "id": "architecture-blocks", "label": "Architecture building blocks", "definition": "The layer-level operations from which neural models are composed and whose shapes determine compute, memory, and data movement.", "includes": [ "Embeddings, attention, position encoding, tokenization", "Convolution, recurrence, normalization" ], "excludes": [ "Kernel implementations of these operations ([[tensor-operators]])", "Efficient attention variants ([[efficient-sequence-models]])" ], "contexts": [ "deep-learning", "generative", "recommendation", "multimodal" ], "concepts": [ "mlsys.embeddings", "mlsys.attention", "mlsys.position-encoding", "mlsys.tokenization", "mlsys.convolution", "mlsys.recurrence", "mlsys.normalization-layer", "mlsys.depthwise-separable-convolution", "mlsys.modality-encoder" ], "related_concepts": [ "mlsys.kv-cache" ], "taught_in": [ { "volume": "vol1", "chapter": "Network Architectures", "url": "https://mlsysbook.ai/vol1/06_nn_architectures/06_nn_architectures.html" }, { "volume": "vol3", "chapter": "The Foundation Model", "url": "https://mlsysbook.ai/vol3/02_foundation_model/02_foundation_model.html" } ] }, { "code": "F2.3", "id": "conditional-computation", "label": "Sparse & conditional computation", "definition": "Model designs in which the computation performed varies per input, such as mixture-of-experts routing and early exit.", "includes": [ "Mixture of experts, routing, capacity, load balancing", "Active vs. total parameters", "Early exit" ], "excludes": [ "Weight pruning and static sparsity ([[model-compression]])", "Cascades across separate models ([[serving-architecture]])" ], "contexts": [ "deep-learning", "generative", "distributed" ], "concepts": [ "mlsys.mixture-of-experts", "mlsys.expert-routing", "mlsys.expert-capacity", "mlsys.load-balancing-loss", "mlsys.active-parameters", "mlsys.early-exit" ], "related_concepts": [ "mlsys.expert-parallel", "mlsys.sparsity" ], "taught_in": [ { "volume": "vol1", "chapter": "Network Architectures", "url": "https://mlsysbook.ai/vol1/06_nn_architectures/06_nn_architectures.html" }, { "volume": "vol1", "chapter": "Model Compression", "url": "https://mlsysbook.ai/vol1/10_model_compression/10_model_compression.html" }, { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" } ] }, { "code": "F2.4", "id": "efficient-sequence-models", "label": "Long-context & efficient sequence models", "definition": "Sequence architectures that change how cost scales with context length, co-designed with the memory and compute they demand.", "includes": [ "Sparse, sliding-window, and linear attention", "State-space models; grouped-query attention", "Context-length extension" ], "excludes": [ "Exact attention kernels such as FlashAttention ([[tensor-operators]])", "KV-cache management ([[llm-inference]])" ], "contexts": [ "generative", "deep-learning" ], "concepts": [ "mlsys.sliding-window-attention", "mlsys.linear-attention", "mlsys.state-space-model", "mlsys.grouped-query-attention", "mlsys.context-extension", "mlsys.context", "mlsys.context-degradation", "mlsys.dynamic-sparse-attention" ], "related_concepts": [ "mlsys.kv-cache", "mlsys.io-aware-attention", "mlsys.sequence-parallel" ], "taught_in": [ { "volume": "vol1", "chapter": "Network Architectures", "url": "https://mlsysbook.ai/vol1/06_nn_architectures/06_nn_architectures.html" } ] }, { "code": "F2.5", "id": "model-artifacts", "label": "Model artifacts & formats", "definition": "The weights, graphs, metadata, and interface contracts that make a trained model a portable, loadable, deployable artifact.", "includes": [ "Weight serialization and exchange formats", "Model export and signatures" ], "excludes": [ "Versioning and registries ([[release-management]])", "Training checkpoints for recovery ([[reliability]])" ], "contexts": [ "edge" ], "concepts": [ "mlsys.model-artifact", "mlsys.weight-serialization", "mlsys.model-exchange-format", "mlsys.model-export", "mlsys.model-signature" ], "related_concepts": [ "mlsys.artifact-version", "mlsys.checkpoint" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Frameworks", "url": "https://mlsysbook.ai/vol1/07_frameworks/07_frameworks.html" }, { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" } ] } ] }, { "code": "F3", "id": "system-models", "label": "Quantitative system models", "question": "Which simple models predict where time, memory, and money go?", "scope": "Analytical and empirical models used to bound performance, capacity, and training cost before or instead of measuring.", "subjects": [ { "code": "F3.1", "id": "roofline-locality", "label": "Roofline, performance models & simulation", "definition": "Bound-based reasoning about whether computation is limited by arithmetic or data movement, and how reuse changes that limit.", "includes": [ "Ridge point and compute- vs. memory-bound regimes", "Energy cost of data movement" ], "excludes": [ "Measured profiles and MFU ([[profiling-utilization]])", "Hardware memory organization ([[memory-systems]])" ], "contexts": [ "deep-learning" ], "concepts": [ "mlsys.arithmetic-intensity", "mlsys.roofline", "mlsys.locality", "mlsys.ridge-point", "mlsys.bound-regime", "mlsys.data-movement-energy", "mlsys.analytical-performance-model", "mlsys.performance-simulation", "mlsys.model-fidelity", "mlsys.execution-trace" ], "related_concepts": [ "mlsys.tiling", "mlsys.memory-bandwidth", "mlsys.compute-throughput" ], "taught_in": [ { "volume": "vol1", "chapter": "Network Architectures", "url": "https://mlsysbook.ai/vol1/06_nn_architectures/06_nn_architectures.html" }, { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" } ] }, { "code": "F3.2", "id": "speedup-limits", "label": "Amdahl's law & scaling efficiency", "definition": "Models of how much speedup added parallel or accelerated resources can deliver, given serial work and fixed or growing problem sizes.", "includes": [ "Amdahl's law and serial fractions", "Strong and weak scaling", "Parallel efficiency" ], "excludes": [ "Communication cost models ([[collectives]])" ], "contexts": [ "distributed" ], "concepts": [ "mlsys.serial-fraction", "mlsys.amdahls-law", "mlsys.strong-scaling", "mlsys.weak-scaling", "mlsys.parallel-efficiency" ], "related_concepts": [ "mlsys.stragglers", "mlsys.communication-overlap" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol2", "chapter": "Introduction", "url": "https://mlsysbook.ai/vol2/01_introduction/01_introduction.html" }, { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" } ] }, { "code": "F3.3", "id": "queueing-models", "label": "Queueing & capacity models", "definition": "Models relating arrivals, service rates, utilization, and waiting, used to predict latency and capacity under load.", "includes": [ "Little's law", "Latency-throughput curves", "Burstiness and variability" ], "excludes": [ "Batching and admission mechanisms ([[request-scheduling]])", "Measuring a specific serving system's latency and throughput ([[latency-goodput]])" ], "contexts": [ "interactive", "streaming" ], "concepts": [ "mlsys.arrival-rate", "mlsys.service-capacity", "mlsys.queueing", "mlsys.littles-law", "mlsys.latency-throughput-curve", "mlsys.arrival-burstiness", "mlsys.finite-buffer" ], "related_concepts": [ "mlsys.tail-latency", "mlsys.batching", "mlsys.utilization" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol3", "chapter": "KV Cache Management", "url": "https://mlsysbook.ai/vol3/05_kv_cache/05_kv_cache.html" } ] }, { "code": "F3.4", "id": "scaling-laws", "label": "Scaling laws & compute-optimal allocation", "definition": "Empirical relationships among parameters, data, and compute and observed quality, used to allocate training budgets.", "includes": [ "Loss scaling laws", "Compute-optimal model/data allocation", "Training compute estimates" ], "excludes": [ "Hyperparameter search ([[experiment-management]])", "Test-time compute scaling ([[test-time-compute]])" ], "contexts": [ "generative", "deep-learning" ], "concepts": [ "mlsys.scaling-laws", "mlsys.compute-optimal-allocation", "mlsys.training-compute-estimate", "mlsys.inference-compute-estimate" ], "related_concepts": [ "mlsys.experiment-budget" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol1", "chapter": "Data Selection", "url": "https://mlsysbook.ai/vol1/09_data_selection/09_data_selection.html" }, { "volume": "vol2", "chapter": "Introduction", "url": "https://mlsysbook.ai/vol2/01_introduction/01_introduction.html" } ] } ] } ] }, { "code": "S", "id": "stack", "label": "Stack", "question": "What does the system run on, from silicon to the fleet?", "areas": [ { "code": "S1", "id": "hardware", "label": "Hardware & accelerators", "question": "How do processors, memory, and chip-level power shape what ML workloads can achieve?", "scope": "Compute, memory, interconnect, and power characteristics of ML hardware from datacenter accelerators to microcontrollers.", "subjects": [ { "code": "S1.1", "id": "accelerator-architecture", "label": "Accelerator architecture", "definition": "The organization of processors specialized for ML: execution units, threading, dataflow, and the throughput they provide.", "includes": [ "GPUs, TPUs, NPUs; SIMT and matrix engines", "Systolic arrays and dataflows", "Domain-specific architectures" ], "excludes": [ "On-device and embedded accelerators ([[edge-embedded-hardware]])", "Compiler mapping onto hardware ([[ml-compilers]])" ], "contexts": [ "deep-learning" ], "concepts": [ "mlsys.accelerator-execution-architecture", "mlsys.compute-throughput", "mlsys.systolic-array", "mlsys.matrix-unit", "mlsys.simt", "mlsys.dataflow", "mlsys.domain-specific-architecture", "mlsys.compute-in-memory", "mlsys.cpu-execution-architecture" ], "related_concepts": [ "mlsys.matrix-multiply" ], "taught_in": [ { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" }, { "volume": "vol2", "chapter": "Compute Infrastructure", "url": "https://mlsysbook.ai/vol2/02_compute_infrastructure/02_compute_infrastructure.html" } ] }, { "code": "S1.2", "id": "memory-systems", "label": "Memory hierarchy & bandwidth", "definition": "The storage levels of an ML processor, from registers and SRAM to HBM, and the capacity and bandwidth limits they impose.", "includes": [ "On-chip SRAM and scratchpads", "HBM", "The memory wall" ], "excludes": [ "Persistent and distributed storage ([[storage-systems]])", "Software memory allocation ([[runtime-dispatch]])" ], "contexts": [ "deep-learning", "generative", "recommendation" ], "concepts": [ "mlsys.memory-hierarchy", "mlsys.memory-capacity", "mlsys.memory-bandwidth", "mlsys.hbm", "mlsys.on-chip-sram", "mlsys.memory-wall", "mlsys.embedding-cache", "mlsys.emerging-nvm" ], "related_concepts": [ "mlsys.locality", "mlsys.tiling" ], "taught_in": [ { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" }, { "volume": "vol2", "chapter": "Compute Infrastructure", "url": "https://mlsysbook.ai/vol2/02_compute_infrastructure/02_compute_infrastructure.html" } ] }, { "code": "S1.3", "id": "node-interconnect", "label": "Host-device & scale-up interconnect", "definition": "Links that move data between hosts and accelerators and among accelerators within a node or rack-scale domain.", "includes": [ "PCIe host links", "NVLink-class scale-up fabrics", "CXL and peer-to-peer transfers" ], "excludes": [ "Datacenter scale-out networks ([[network-fabrics]])" ], "contexts": [ "distributed" ], "concepts": [ "mlsys.host-device-interconnect", "mlsys.scale-up-interconnect", "mlsys.coherent-interconnect", "mlsys.peer-to-peer-transfer" ], "related_concepts": [ "mlsys.network-topology", "mlsys.collectives" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol2", "chapter": "Compute Infrastructure", "url": "https://mlsysbook.ai/vol2/02_compute_infrastructure/02_compute_infrastructure.html" }, { "volume": "vol2", "chapter": "Network Fabrics", "url": "https://mlsysbook.ai/vol2/03_network_fabrics/03_network_fabrics.html" } ] }, { "code": "S1.4", "id": "chip-power-thermal", "label": "Chip power & thermal limits", "definition": "Electrical and thermal limits at the chip and board level, and the runtime controls that keep execution within them.", "includes": [ "TDP and thermal throttling", "DVFS and power capping", "Dark silicon" ], "excludes": [ "Facility power and cooling ([[datacenter-facilities]])", "Carbon accounting ([[sustainability-cost]])" ], "contexts": [ "edge", "physical" ], "concepts": [ "mlsys.power-delivery", "mlsys.thermal-dynamics", "mlsys.tdp", "mlsys.dvfs", "mlsys.dark-silicon", "mlsys.power-capping", "mlsys.duty-cycling" ], "related_concepts": [ "mlsys.energy" ], "taught_in": [ { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" }, { "volume": "vol2", "chapter": "Compute Infrastructure", "url": "https://mlsysbook.ai/vol2/02_compute_infrastructure/02_compute_infrastructure.html" }, { "volume": "vol4", "chapter": "Workload Placement", "url": "https://mlsysbook.ai/vol4/13_placement/13_placement.html" } ] }, { "code": "S1.5", "id": "edge-embedded-hardware", "label": "Edge & embedded hardware", "definition": "Hardware for on-device ML, from mobile SoCs to microcontrollers, where memory and energy per inference bind.", "includes": [ "Microcontrollers and TinyML devices", "Heterogeneous mobile SoCs and NPUs", "Energy and memory budgets" ], "excludes": [ "On-device runtimes and OTA updates ([[on-device-deployment]])", "Robot-specific real-time platforms ([[real-time-systems]])" ], "contexts": [ "edge", "physical" ], "concepts": [ "mlsys.microcontroller", "mlsys.heterogeneous-soc", "mlsys.edge-npu", "mlsys.on-device-memory-budget", "mlsys.intermittent-computing", "mlsys.sensor-interconnect" ], "related_concepts": [ "mlsys.quantization", "mlsys.placement", "mlsys.energy-per-inference" ], "taught_in": [ { "volume": "vol1", "chapter": "System Paradigms", "url": "https://mlsysbook.ai/vol1/02_system_paradigms/02_system_paradigms.html" }, { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" }, { "volume": "vol2", "chapter": "Edge Intelligence", "url": "https://mlsysbook.ai/vol2/11_edge_intelligence/11_edge_intelligence.html" }, { "volume": "vol4", "chapter": "Workload Placement", "url": "https://mlsysbook.ai/vol4/13_placement/13_placement.html" } ] } ] }, { "code": "S2", "id": "software-stack", "label": "Frameworks, compilers & runtimes", "question": "How is a model program expressed, transformed, and executed efficiently on hardware?", "scope": "The software layers between model code and hardware: programming models, operators, compilers, runtimes, and numerics.", "subjects": [ { "code": "S2.1", "id": "programming-models", "label": "ML frameworks & programming models", "definition": "The abstractions with which ML programs are written: tensors, computation graphs, execution modes, and automatic differentiation.", "includes": [ "Tensors and computation graphs", "Eager vs. graph execution; tracing JIT", "Automatic differentiation" ], "excludes": [ "Compiler optimization passes ([[ml-compilers]])", "Framework product catalogs" ], "contexts": [ "deep-learning" ], "concepts": [ "mlsys.autodiff", "mlsys.tensor", "mlsys.computation-graph", "mlsys.eager-execution", "mlsys.tracing-jit", "mlsys.function-transformations", "mlsys.task-actor-runtime", "mlsys.single-vs-multi-controller", "mlsys.embedded-dsl", "mlsys.differentiable-programming", "mlsys.llm-training-framework", "mlsys.python-gil" ], "related_concepts": [ "mlsys.tensor-layout" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Frameworks", "url": "https://mlsysbook.ai/vol1/07_frameworks/07_frameworks.html" } ] }, { "code": "S2.2", "id": "tensor-operators", "label": "Tensor operators & memory layout", "definition": "Implementations of core tensor operations, such as matrix multiply, convolution, and attention, and the layouts that govern their efficiency.", "includes": [ "GEMM and convolution lowering", "IO-aware attention kernels", "Vendor kernel libraries" ], "excludes": [ "Compiler-generated kernels and autotuning ([[ml-compilers]])", "Architectural changes to attention ([[efficient-sequence-models]])" ], "contexts": [ "deep-learning", "generative" ], "concepts": [ "mlsys.tensor-layout", "mlsys.matrix-multiply", "mlsys.io-aware-attention", "mlsys.convolution-lowering", "mlsys.kernel-library", "mlsys.sparse-tensor-kernels", "mlsys.sparse-ragged-tensors" ], "related_concepts": [ "mlsys.fusion", "mlsys.tiling" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" } ] }, { "code": "S2.3", "id": "gpu-programming", "label": "Kernel & GPU programming model", "definition": "How kernels are written for parallel accelerators: the thread hierarchy, memory access patterns, occupancy, asynchronous data movement, and programming the matrix units.", "includes": [ "Writing and tuning kernels in CUDA-class or tile-level languages such as Triton", "Shared memory, synchronization, and latency hiding" ], "excludes": [ "Choosing kernels for operators ([[tensor-operators]])", "Automatic kernel generation and fusion ([[ml-compilers]])" ], "contexts": [ "deep-learning" ], "concepts": [ "mlsys.thread-hierarchy", "mlsys.memory-coalescing", "mlsys.occupancy", "mlsys.async-data-movement", "mlsys.matrix-engine-programming" ], "related_concepts": [ "mlsys.simt", "mlsys.tiling", "mlsys.io-aware-attention" ], "taught_in": [ { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" }, { "volume": "vol2", "chapter": "Fleet Efficiency", "url": "https://mlsysbook.ai/vol2/09_performance_engineering/09_performance_engineering.html" } ] }, { "code": "S2.4", "id": "ml-compilers", "label": "ML compilers & kernel optimization", "definition": "Transforming model programs through intermediate representations into optimized kernels for target hardware.", "includes": [ "Graph and loop IRs; lowering", "Fusion, tiling, autotuning, kernel DSLs", "Static memory planning" ], "excludes": [ "Quantization as a compression technique ([[model-compression]])" ], "contexts": [ "deep-learning", "edge" ], "concepts": [ "mlsys.compilation", "mlsys.fusion", "mlsys.tiling", "mlsys.intermediate-representation", "mlsys.autotuning", "mlsys.kernel-dsl", "mlsys.memory-planning", "mlsys.persistent-kernel", "mlsys.graph-superoptimization", "mlsys.dynamic-shapes", "mlsys.rule-based-graph-rewriting" ], "related_concepts": [ "mlsys.dispatch" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Frameworks", "url": "https://mlsysbook.ai/vol1/07_frameworks/07_frameworks.html" }, { "volume": "vol1", "chapter": "Hardware Acceleration", "url": "https://mlsysbook.ai/vol1/11_hw_acceleration/11_hw_acceleration.html" }, { "volume": "vol2", "chapter": "Fleet Efficiency", "url": "https://mlsysbook.ai/vol2/09_performance_engineering/09_performance_engineering.html" } ] }, { "code": "S2.5", "id": "runtime-dispatch", "label": "Runtime execution & dispatch", "definition": "How a runtime launches and orders device work and manages device memory during execution.", "includes": [ "Launch overhead and graph replay", "Streams and asynchronous execution", "Caching allocators" ], "excludes": [ "Request-level serving schedulers ([[request-scheduling]])" ], "contexts": [ "deep-learning", "interactive" ], "concepts": [ "mlsys.dispatch", "mlsys.device-streams", "mlsys.caching-allocator", "mlsys.graph-replay", "mlsys.cpu-threading-numa", "mlsys.execution-providers", "mlsys.memory-fragmentation", "mlsys.mmap-weights", "mlsys.os-memory-pressure", "mlsys.managed-runtime-interop" ], "related_concepts": [ "mlsys.communication-overlap" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Frameworks", "url": "https://mlsysbook.ai/vol1/07_frameworks/07_frameworks.html" }, { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Fleet Efficiency", "url": "https://mlsysbook.ai/vol2/09_performance_engineering/09_performance_engineering.html" } ] }, { "code": "S2.6", "id": "data-loading", "label": "Data loading & input pipelines", "definition": "Delivering training and inference inputs to accelerators fast enough: host-side loading and preprocessing, prefetching, and sampling minibatches from large or irregular data such as graphs.", "includes": [ "Data-loader workers, decoding and augmentation on the host, pinned memory, and prefetching", "Minibatch construction for irregular data, such as graph neighborhood sampling" ], "excludes": [ "Building datasets and transformation pipelines ([[data-pipelines]])", "Storage systems and parallel file systems that hold the data ([[storage-systems]])" ], "contexts": [ "deep-learning", "interactive" ], "concepts": [ "mlsys.input-pipeline", "mlsys.input-prefetching", "mlsys.neighbor-sampling", "mlsys.host-data-loader", "mlsys.zero-copy-dma" ], "related_concepts": [], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol2", "chapter": "Data Storage", "url": "https://mlsysbook.ai/vol2/04_data_storage/04_data_storage.html" } ] }, { "code": "S2.7", "id": "numerics", "label": "Numerical formats & precision", "definition": "Number formats used in ML computation and the techniques that keep training and inference accurate at reduced precision.", "includes": [ "FP32/BF16/FP8/INT formats and block scaling", "Mixed precision and loss scaling", "Accumulation precision" ], "excludes": [ "Post-training quantization as compression ([[model-compression]])" ], "contexts": [ "deep-learning", "generative" ], "concepts": [ "mlsys.precision", "mlsys.mixed-precision", "mlsys.numerical-stability", "mlsys.loss-scaling", "mlsys.block-scaled-format", "mlsys.accumulation-precision", "mlsys.numerical-nondeterminism", "mlsys.batch-invariance" ], "related_concepts": [ "mlsys.quantization" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol1", "chapter": "Model Compression", "url": "https://mlsysbook.ai/vol1/10_model_compression/10_model_compression.html" }, { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" } ] } ] }, { "code": "S3", "id": "distributed", "label": "Parallelism & distributed execution", "question": "How is one workload split across many devices, and what does coordination cost?", "scope": "Partitioning ML work across devices and participants, the communication it requires, and the coordination that keeps it consistent.", "subjects": [ { "code": "S3.1", "id": "parallelism", "label": "Parallelism strategies", "definition": "Ways of partitioning a model's computation and state across devices, and how those dimensions are combined.", "includes": [ "Data, tensor, pipeline, sequence, expert parallelism", "ZeRO/FSDP state sharding", "Hybrid 3D layouts and pipeline bubbles" ], "excludes": [ "Collective algorithms ([[collectives]])", "Job placement on the cluster ([[cluster-scheduling]])", "Searching for and placing a parallel plan ([[parallelism-planning]])" ], "contexts": [ "distributed", "generative", "recommendation" ], "concepts": [ "mlsys.data-parallel", "mlsys.tensor-parallel", "mlsys.pipeline-parallel", "mlsys.state-sharding", "mlsys.expert-parallel", "mlsys.sequence-parallel", "mlsys.hybrid-parallel", "mlsys.pipeline-bubble", "mlsys.embedding-table-sharding", "mlsys.parameter-server", "mlsys.sync-async-training", "mlsys.gnn-graph-partitioning" ], "related_concepts": [ "mlsys.collectives", "mlsys.training-state", "mlsys.neighbor-sampling" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" } ] }, { "code": "S3.2", "id": "parallelism-planning", "label": "Parallelization planning & placement", "definition": "Choosing and changing how a model is partitioned across devices: describing devices as a mesh, searching or propagating sharding decisions, placing experts and stages on heterogeneous hardware, and resharding at run time.", "includes": [ "Automatic parallelization and sharding propagation", "Device meshes, expert placement, and dynamic resharding" ], "excludes": [ "The parallelism strategies themselves ([[parallelism]])" ], "contexts": [ "distributed", "generative", "recommendation" ], "concepts": [ "mlsys.auto-parallelization", "mlsys.sharding-propagation", "mlsys.device-mesh", "mlsys.heterogeneous-accelerator-execution", "mlsys.dynamic-resharding", "mlsys.expert-placement" ], "related_concepts": [ "mlsys.hybrid-parallel" ], "taught_in": [ { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" } ] }, { "code": "S3.3", "id": "collectives", "label": "Collective communication", "definition": "Group communication primitives and algorithms used by parallel ML, their cost models, and techniques to reduce or hide their cost.", "includes": [ "All-reduce, all-gather, all-to-all", "Ring and tree algorithms; alpha-beta cost model", "Overlap and gradient compression" ], "excludes": [ "Physical fabric design ([[network-fabrics]])" ], "contexts": [ "distributed", "recommendation" ], "concepts": [ "mlsys.collectives", "mlsys.communication-overlap", "mlsys.stragglers", "mlsys.ring-allreduce", "mlsys.tree-allreduce", "mlsys.all-to-all", "mlsys.alpha-beta-model", "mlsys.gradient-compression", "mlsys.local-sgd", "mlsys.process-group-rendezvous" ], "related_concepts": [ "mlsys.network-topology", "mlsys.bisection-bandwidth", "mlsys.in-network-reduction" ], "taught_in": [ { "volume": "vol2", "chapter": "Collective Communication", "url": "https://mlsysbook.ai/vol2/05_collective_communication/05_collective_communication.html" }, { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" } ] }, { "code": "S3.4", "id": "coordination", "label": "Coordination & consensus", "definition": "Mechanisms by which distributed components agree on authoritative control state and tolerate stale or failed participants.", "includes": [ "Consensus and leader election", "Leases and fencing", "Bounded-staleness consistency" ], "excludes": [ "Checkpoint and recovery protocols ([[reliability]])", "Retry and compensation semantics ([[failure-semantics]])" ], "contexts": [ "distributed", "agentic" ], "concepts": [ "mlsys.consensus", "mlsys.fencing", "mlsys.lease", "mlsys.leader-election", "mlsys.bounded-staleness", "mlsys.cap-tradeoff" ], "related_concepts": [ "mlsys.checkpoint", "mlsys.consistent-cut", "mlsys.idempotency" ], "taught_in": [ { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" }, { "volume": "vol2", "chapter": "Fault Tolerance", "url": "https://mlsysbook.ai/vol2/07_fault_tolerance/07_fault_tolerance.html" }, { "volume": "vol3", "chapter": "Durable Execution", "url": "https://mlsysbook.ai/vol3/11_durable_execution/11_durable_execution.html" }, { "volume": "vol3", "chapter": "Multi-Agent Coordination", "url": "https://mlsysbook.ai/vol3/18_multi_agent/18_multi_agent.html" } ] }, { "code": "S3.5", "id": "federated-learning", "label": "Federated & decentralized learning", "definition": "Training across devices or organizations that keep their data local, coordinated through a shared model.", "includes": [ "Federated averaging", "Client heterogeneity and selection", "Cross-device vs. cross-silo" ], "excludes": [ "Secure aggregation and differential privacy ([[security-privacy]])" ], "contexts": [ "federated", "edge" ], "concepts": [ "mlsys.federated-learning", "mlsys.federated-averaging", "mlsys.non-iid-data", "mlsys.client-sampling", "mlsys.federation-setting", "mlsys.gossip-averaging" ], "related_concepts": [ "mlsys.differential-privacy", "mlsys.privacy" ], "taught_in": [ { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol2", "chapter": "Edge Intelligence", "url": "https://mlsysbook.ai/vol2/11_edge_intelligence/11_edge_intelligence.html" } ] } ] }, { "code": "S4", "id": "infrastructure-fleet", "label": "Datacenter infrastructure & fleet", "question": "How are AI datacenters built, connected, fed, and shared across many jobs?", "scope": "Facilities, networks, storage, and cluster management that turn many accelerators into a shared ML fleet.", "subjects": [ { "code": "S4.1", "id": "datacenter-facilities", "label": "Datacenter power, cooling & facilities", "definition": "Engineering of the buildings that house ML fleets: power provisioning, cooling, density, and facility efficiency.", "includes": [ "Facility power and oversubscription", "Air and liquid cooling; rack density", "PUE" ], "excludes": [ "Carbon and water accounting ([[sustainability-cost]])", "Chip-level power limits ([[chip-power-thermal]])" ], "contexts": [ "distributed" ], "concepts": [ "mlsys.pue", "mlsys.liquid-cooling", "mlsys.rack-power-density", "mlsys.power-oversubscription", "mlsys.warehouse-scale-computer", "mlsys.training-power-swings", "mlsys.facility-energy-storage", "mlsys.grid-interconnection", "mlsys.on-site-generation" ], "related_concepts": [ "mlsys.energy", "mlsys.carbon", "mlsys.power-delivery", "mlsys.thermal-dynamics" ], "taught_in": [ { "volume": "vol2", "chapter": "Compute Infrastructure", "url": "https://mlsysbook.ai/vol2/02_compute_infrastructure/02_compute_infrastructure.html" }, { "volume": "vol2", "chapter": "Sustainable AI", "url": "https://mlsysbook.ai/vol2/15_sustainable_ai/15_sustainable_ai.html" } ] }, { "code": "S4.2", "id": "network-fabrics", "label": "Network fabrics", "definition": "Datacenter networks that connect accelerator nodes: transports, topologies, flow and congestion control, and in-network compute.", "includes": [ "RDMA over InfiniBand and RoCE; lossless flow control", "Fat-tree, rail-optimized, torus topologies", "Congestion control; in-network reduction" ], "excludes": [ "Intra-node links ([[node-interconnect]])", "Collective algorithms ([[collectives]])" ], "contexts": [ "distributed" ], "concepts": [ "mlsys.network-topology", "mlsys.rdma", "mlsys.lossless-fabric", "mlsys.congestion-control", "mlsys.fat-tree", "mlsys.rail-optimized", "mlsys.bisection-bandwidth", "mlsys.in-network-reduction", "mlsys.adaptive-routing", "mlsys.incast", "mlsys.network-propagation-delay" ], "related_concepts": [ "mlsys.collectives", "mlsys.all-to-all" ], "taught_in": [ { "volume": "vol2", "chapter": "Network Fabrics", "url": "https://mlsysbook.ai/vol2/03_network_fabrics/03_network_fabrics.html" } ] }, { "code": "S4.3", "id": "storage-systems", "label": "Storage & data delivery", "definition": "Storage systems that hold training data, checkpoints, and artifacts at scale and deliver them to accelerators fast enough.", "includes": [ "Parallel file systems and object stores", "Input pipelines and data stalls", "Checkpoint I/O and storage tiering" ], "excludes": [ "Dataset formats and schema ([[data-pipelines]])", "Checkpoint frequency and recovery semantics ([[reliability]])", "Host-side loading and prefetching of batches ([[data-loading]])" ], "contexts": [ "distributed", "batch" ], "concepts": [ "mlsys.parallel-file-system", "mlsys.object-storage", "mlsys.checkpoint-io", "mlsys.storage-tiering" ], "related_concepts": [ "mlsys.checkpoint", "mlsys.dataset-storage-layout", "mlsys.data-ingestion", "mlsys.input-pipeline" ], "taught_in": [ { "volume": "vol1", "chapter": "Data Engineering", "url": "https://mlsysbook.ai/vol1/04_data_engineering/04_data_engineering.html" }, { "volume": "vol2", "chapter": "Data Storage", "url": "https://mlsysbook.ai/vol2/04_data_storage/04_data_storage.html" } ] }, { "code": "S4.4", "id": "cluster-scheduling", "label": "Cluster scheduling & orchestration", "definition": "Allocating a shared cluster's accelerators to jobs over time: placement, gang allocation, fairness, preemption, and elasticity.", "includes": [ "Gang scheduling and backfill", "Fair sharing, preemption, elastic jobs", "Topology-aware placement and bin packing" ], "excludes": [ "Request-level serving autoscaling ([[request-scheduling]])", "Workflow DAG orchestration ([[pipelines-reproducibility]])", "Packaging code and dependencies as images ([[pipelines-reproducibility]])" ], "contexts": [ "distributed", "batch" ], "concepts": [ "mlsys.gang-scheduling", "mlsys.job-preemption", "mlsys.dominant-resource-fairness", "mlsys.elastic-training", "mlsys.topology-aware-placement", "mlsys.bin-packing", "mlsys.ml-aware-scheduling", "mlsys.cluster-manager" ], "related_concepts": [ "mlsys.admission", "mlsys.utilization", "mlsys.checkpoint" ], "taught_in": [ { "volume": "vol2", "chapter": "Fleet Orchestration", "url": "https://mlsysbook.ai/vol2/08_fleet_orchestration/08_fleet_orchestration.html" } ] }, { "code": "S4.5", "id": "capacity-provisioning", "label": "Capacity planning & multi-tenancy", "definition": "Provisioning fleet capacity and sharing it among tenants through quotas, accelerator sharing, and interference control.", "includes": [ "Capacity planning and quotas", "Accelerator sharing and partitioning", "Spot capacity and interference" ], "excludes": [ "Security isolation guarantees ([[access-isolation]])", "Unit-cost economics ([[sustainability-cost]])" ], "contexts": [ "distributed", "interactive" ], "concepts": [ "mlsys.capacity-planning", "mlsys.multi-tenancy", "mlsys.resource-quota", "mlsys.gpu-partitioning", "mlsys.spot-capacity", "mlsys.performance-interference", "mlsys.cloud-service-model" ], "related_concepts": [ "mlsys.isolation", "mlsys.autoscaling", "mlsys.unit-cost" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Fleet Orchestration", "url": "https://mlsysbook.ai/vol2/08_fleet_orchestration/08_fleet_orchestration.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" } ] } ] } ] }, { "code": "P", "id": "lifecycle", "label": "Lifecycle", "question": "What do engineers do to build, ship, and keep the system working?", "areas": [ { "code": "P1", "id": "data", "label": "Data engineering", "question": "How is data acquired, labeled, curated, validated, stored, and kept trustworthy?", "scope": "Acquisition and labeling, corpus curation, validation and features, pipelines and storage formats, lineage, and temporal correctness of training and serving data.", "subjects": [ { "code": "P1.1", "id": "data-collection", "label": "Data collection & labeling", "definition": "Obtaining observations and supervisory targets whose sampling, coverage, and label quality are known and fit the task.", "includes": [ "Sampling design, human and programmatic labeling, labeling budgets.", "Annotation quality and agreement." ], "excludes": [ "Sensor design and calibration ([[perception-sensing]])", "Corpus filtering and mixtures ([[data-curation]])" ], "contexts": [ "classical-ml", "deep-learning", "physical" ], "concepts": [ "mlsys.data-acquisition", "mlsys.labels", "mlsys.weak-supervision", "mlsys.active-learning", "mlsys.annotator-agreement", "mlsys.class-imbalance", "mlsys.rebalancing" ], "related_concepts": [ "mlsys.sensing", "mlsys.privacy" ], "taught_in": [ { "volume": "vol1", "chapter": "Data Engineering", "url": "https://mlsysbook.ai/vol1/04_data_engineering/04_data_engineering.html" }, { "volume": "vol3", "chapter": "Trajectory Curation", "url": "https://mlsysbook.ai/vol3/15_trajectory_curation/15_trajectory_curation.html" }, { "volume": "vol4", "chapter": "Physical Data", "url": "https://mlsysbook.ai/vol4/05_data/05_data.html" } ] }, { "code": "P1.2", "id": "data-curation", "label": "Data selection & curation", "definition": "Choosing, filtering, deduplicating, mixing, augmenting, and synthesizing training examples to maximize learning value per unit of compute and labeling cost.", "includes": [ "Web-scale corpus filtering, deduplication, and domain mixtures.", "Augmentation and synthetic or model-generated data.", "Trajectory and demonstration curation for agents and robots." ], "excludes": [ "Labeling processes ([[data-collection]])", "Teacher-student compression ([[model-compression]])" ], "contexts": [ "deep-learning", "generative", "agentic", "multimodal" ], "concepts": [ "mlsys.data-selection", "mlsys.deduplication", "mlsys.quality-filtering", "mlsys.data-mixture", "mlsys.synthetic-data", "mlsys.data-augmentation", "mlsys.data-valuation" ], "related_concepts": [ "mlsys.scaling-laws", "mlsys.distillation", "mlsys.benchmark-contamination" ], "taught_in": [ { "volume": "vol1", "chapter": "Data Selection", "url": "https://mlsysbook.ai/vol1/09_data_selection/09_data_selection.html" }, { "volume": "vol3", "chapter": "Trajectory Curation", "url": "https://mlsysbook.ai/vol3/15_trajectory_curation/15_trajectory_curation.html" }, { "volume": "vol4", "chapter": "Physical Data", "url": "https://mlsysbook.ai/vol4/05_data/05_data.html" } ] }, { "code": "P1.3", "id": "data-quality", "label": "Data validation & feature engineering", "definition": "Checking that recorded data is fit for use and transforming it into model inputs whose semantics and availability match training and serving.", "includes": [ "Schema and distribution checks before training and serving.", "Feature construction and shared feature stores." ], "excludes": [ "Learned representations inside the model ([[models-architectures]])", "Production drift monitoring ([[monitoring]])" ], "contexts": [ "classical-ml", "batch", "streaming", "recommendation" ], "concepts": [ "mlsys.data-quality", "mlsys.features", "mlsys.missing-data", "mlsys.data-validation", "mlsys.feature-store", "mlsys.data-cleaning", "mlsys.feature-hashing", "mlsys.feature-cross", "mlsys.exploratory-data-analysis", "mlsys.data-cascade" ], "related_concepts": [ "mlsys.data-contract", "mlsys.train-serve-skew" ], "taught_in": [ { "volume": "vol1", "chapter": "Data Engineering", "url": "https://mlsysbook.ai/vol1/04_data_engineering/04_data_engineering.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" } ] }, { "code": "P1.4", "id": "data-pipelines", "label": "Data pipelines & storage formats", "definition": "Engineering ingestion, batch and stream transformation, event-time semantics, storage formats, and schemas for training and serving data.", "includes": [ "ETL/ELT, batch and stream processing, columnar formats.", "Data loaders that keep accelerators fed." ], "excludes": [ "Tensor layout in working memory ([[tensor-operators]])", "Delivering batches to accelerators ([[data-loading]])" ], "contexts": [ "batch", "streaming", "distributed" ], "concepts": [ "mlsys.dataset-storage-layout", "mlsys.data-ingestion", "mlsys.schema-evolution", "mlsys.stream-processing", "mlsys.feature-backfill", "mlsys.batch-data-processing", "mlsys.lakehouse", "mlsys.event-time-windowing", "mlsys.delivery-semantics" ], "related_concepts": [ "mlsys.workflow-orchestration", "mlsys.input-pipeline" ], "taught_in": [ { "volume": "vol1", "chapter": "Data Engineering", "url": "https://mlsysbook.ai/vol1/04_data_engineering/04_data_engineering.html" }, { "volume": "vol2", "chapter": "Data Storage", "url": "https://mlsysbook.ai/vol2/04_data_storage/04_data_storage.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" } ] }, { "code": "P1.5", "id": "data-lineage", "label": "Data contracts, versioning & lineage", "definition": "Specifying, versioning, and tracing the datasets a system consumes so that dependencies, provenance, and changes are explicit.", "includes": [ "Dataset versions, snapshots, and provenance records.", "Producer-consumer contracts and dependency hygiene." ], "excludes": [ "Model and code artifact versioning ([[release-management]])", "Dataset documentation such as datasheets ([[transparency-explainability]])" ], "contexts": [], "concepts": [ "mlsys.lineage", "mlsys.data-contract", "mlsys.data-versioning", "mlsys.data-dependency", "mlsys.data-governance", "mlsys.data-consent", "mlsys.data-model-licensing", "mlsys.pii-deidentification" ], "related_concepts": [ "mlsys.artifact-version", "mlsys.experiment-reproducibility" ], "taught_in": [ { "volume": "vol1", "chapter": "Data Engineering", "url": "https://mlsysbook.ai/vol1/04_data_engineering/04_data_engineering.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" }, { "volume": "vol4", "chapter": "Physical Data", "url": "https://mlsysbook.ai/vol4/05_data/05_data.html" } ] }, { "code": "P1.6", "id": "data-leakage-skew", "label": "Leakage & training-serving skew", "definition": "Ensuring examples use only information available at prediction time and that training and serving compute features identically and equally fresh.", "includes": [ "Point-in-time joins and temporal leakage.", "Feature staleness and skew between offline and online paths." ], "excludes": [ "Deployment distribution change ([[drift-retraining]])" ], "contexts": [ "classical-ml", "streaming" ], "concepts": [ "mlsys.leakage", "mlsys.point-in-time", "mlsys.train-serve-skew", "mlsys.feature-freshness", "mlsys.online-feature-consistency" ], "related_concepts": [ "mlsys.feature-store", "mlsys.benchmark-contamination" ], "taught_in": [ { "volume": "vol1", "chapter": "Data Engineering", "url": "https://mlsysbook.ai/vol1/04_data_engineering/04_data_engineering.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" }, { "volume": "vol3", "chapter": "Trajectory Curation", "url": "https://mlsysbook.ai/vol3/15_trajectory_curation/15_trajectory_curation.html" } ] } ] }, { "code": "P2", "id": "training", "label": "Training & adaptation", "question": "How are models trained and adapted, and what does that demand from the system?", "scope": "Learning paradigms, optimization dynamics, training memory, experiment management, fine-tuning, post-training, and continual learning.", "subjects": [ { "code": "P2.1", "id": "learning-paradigms", "label": "Learning paradigms", "definition": "The ways supervision, self-generated targets, interaction, and demonstration produce learning signals, each with distinct data and compute demands.", "includes": [ "Supervised, self-supervised, unsupervised, reinforcement, and imitation learning." ], "excludes": [ "Preference and RL post-training of pretrained models ([[post-training]])" ], "contexts": [ "classical-ml", "deep-learning", "generative", "physical" ], "concepts": [ "mlsys.supervised-learning", "mlsys.self-supervised-learning", "mlsys.unsupervised-learning", "mlsys.reinforcement-learning", "mlsys.imitation-learning", "mlsys.pretraining", "mlsys.causal-lm", "mlsys.compounding-error", "mlsys.interactive-imitation", "mlsys.offline-rl" ], "related_concepts": [ "mlsys.federated-learning", "mlsys.in-context-learning" ], "taught_in": [ { "volume": "vol1", "chapter": "Neural Computation", "url": "https://mlsysbook.ai/vol1/05_nn_computation/05_nn_computation.html" }, { "volume": "vol1", "chapter": "Data Selection", "url": "https://mlsysbook.ai/vol1/09_data_selection/09_data_selection.html" }, { "volume": "vol4", "chapter": "Policy Training", "url": "https://mlsysbook.ai/vol4/06_training/06_training.html" } ] }, { "code": "P2.2", "id": "training-optimization", "label": "Optimization & training dynamics", "definition": "The objectives, update rules, schedules, and batch regimes that determine whether and how efficiently training converges.", "includes": [ "Optimizers, learning-rate warmup and decay, large-batch scaling.", "Loss spikes, divergence, and their mitigations." ], "excludes": [ "Floating-point formats and overflow ([[numerics]])" ], "contexts": [ "deep-learning", "distributed" ], "concepts": [ "mlsys.loss", "mlsys.optimizer", "mlsys.regularization", "mlsys.batch-size", "mlsys.learning-rate-schedule", "mlsys.critical-batch-size", "mlsys.training-instability", "mlsys.mode-collapse", "mlsys.gradient-clipping", "mlsys.weight-initialization" ], "related_concepts": [ "mlsys.numerical-stability", "mlsys.mixed-precision", "mlsys.data-parallel" ], "taught_in": [ { "volume": "vol1", "chapter": "Neural Computation", "url": "https://mlsysbook.ai/vol1/05_nn_computation/05_nn_computation.html" }, { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" } ] }, { "code": "P2.3", "id": "training-memory", "label": "Training memory & state", "definition": "Accounting for and reducing the parameters, gradients, optimizer state, and activations a training step must hold.", "includes": [ "Activation recomputation, gradient accumulation, CPU/NVMe offload." ], "excludes": [ "Sharding state across devices ([[parallelism]])", "Persisting state for recovery ([[checkpoint-recovery]])" ], "contexts": [ "deep-learning", "distributed" ], "concepts": [ "mlsys.training-state", "mlsys.activation-recompute", "mlsys.gradient-accumulation", "mlsys.memory-offloading", "mlsys.memory-efficient-optimizer" ], "related_concepts": [ "mlsys.state-sharding", "mlsys.checkpoint", "mlsys.memory-capacity", "mlsys.neighbor-sampling" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" } ] }, { "code": "P2.4", "id": "experiment-management", "label": "Hyperparameter search & experiment management", "definition": "Allocating a finite budget of training runs across configurations, and recording runs so their outcomes can be compared and reproduced.", "includes": [ "Random, Bayesian, and multi-fidelity search; small-scale proxies.", "Run metadata and experiment tracking." ], "excludes": [ "Pipeline orchestration ([[pipelines-reproducibility]])" ], "contexts": [ "deep-learning" ], "concepts": [ "mlsys.hyperparameter-search", "mlsys.experiment-budget", "mlsys.experiment-tracking", "mlsys.hyperparameter-transfer", "mlsys.model-selection" ], "related_concepts": [ "mlsys.selection-bias", "mlsys.experiment-reproducibility", "mlsys.scaling-laws" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" } ] }, { "code": "P2.5", "id": "fine-tuning", "label": "Adaptation: fine-tuning, prompting & transfer", "definition": "Adapting a pretrained model to a new task or behavior, by continued training of all or part of its parameters or by prompting without parameter updates.", "includes": [ "Instruction and supervised fine-tuning, adapters and LoRA." ], "excludes": [ "Preference or reward-driven training ([[post-training]])", "Assembling context for agents ([[context-memory]])" ], "contexts": [ "deep-learning", "generative", "agentic", "edge" ], "concepts": [ "mlsys.transfer-learning", "mlsys.peft", "mlsys.supervised-fine-tuning", "mlsys.lora", "mlsys.prompt-engineering", "mlsys.loss-masking", "mlsys.in-context-learning" ], "related_concepts": [ "mlsys.distillation", "mlsys.quantization", "mlsys.catastrophic-forgetting" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol2", "chapter": "Edge Intelligence", "url": "https://mlsysbook.ai/vol2/11_edge_intelligence/11_edge_intelligence.html" }, { "volume": "vol3", "chapter": "Trajectory Fine-Tuning", "url": "https://mlsysbook.ai/vol3/16_fine_tuning/16_fine_tuning.html" } ] }, { "code": "P2.6", "id": "post-training", "label": "Preference & RL post-training", "definition": "Training pretrained models against preference, reward-model, or verifiable-reward signals, and the rollout, scoring, and weight-synchronization systems that loop requires.", "includes": [ "RLHF, DPO-style preference optimization, RL with verifiable rewards.", "Rollout generation, reward scoring, colocated vs disaggregated actor/learner, weight sync." ], "excludes": [ "General RL theory ([[learning-paradigms]])", "Reward exploitation as a specification failure ([[objectives-decisions]])" ], "contexts": [ "generative", "agentic", "distributed" ], "concepts": [ "mlsys.rlhf", "mlsys.reward-model", "mlsys.preference-optimization", "mlsys.rlvr", "mlsys.rl-rollout", "mlsys.weight-sync", "mlsys.policy-staleness", "mlsys.credit-assignment", "mlsys.reward-hacking" ], "related_concepts": [ "mlsys.reinforcement-learning", "mlsys.verifier", "mlsys.speculative-decoding" ], "taught_in": [ { "volume": "vol2", "chapter": "Distributed Training", "url": "https://mlsysbook.ai/vol2/06_distributed_training/06_distributed_training.html" }, { "volume": "vol3", "chapter": "Trajectory Fine-Tuning", "url": "https://mlsysbook.ai/vol3/16_fine_tuning/16_fine_tuning.html" }, { "volume": "vol3", "chapter": "Reinforcement Learning from Verifiable Rewards", "url": "https://mlsysbook.ai/vol3/17_rlvr/17_rlvr.html" } ] }, { "code": "P2.7", "id": "continual-learning", "label": "Continual & online learning", "definition": "Updating deployed models incrementally from new or streaming data while retaining prior competence.", "includes": [ "Online and incremental updates, forgetting and replay.", "Personalization and adaptation on device." ], "excludes": [ "Deciding when to retrain ([[drift-retraining]])", "Federated aggregation protocols ([[federated-learning]])" ], "contexts": [ "streaming", "edge", "federated" ], "concepts": [ "mlsys.online-learning", "mlsys.incremental-training", "mlsys.catastrophic-forgetting", "mlsys.experience-replay", "mlsys.on-device-training" ], "related_concepts": [ "mlsys.retraining", "mlsys.distribution-shift", "mlsys.federated-learning" ], "taught_in": [ { "volume": "vol2", "chapter": "Edge Intelligence", "url": "https://mlsysbook.ai/vol2/11_edge_intelligence/11_edge_intelligence.html" } ] } ] }, { "code": "P3", "id": "evaluation", "label": "Model evaluation & testing", "question": "How do we know a model and the system around it behave well enough, and for whom?", "scope": "Evaluation design, metrics and uncertainty, slices and robustness, evaluation of generative and agentic outputs, and ML testing and debugging.", "subjects": [ { "code": "P3.1", "id": "evaluation-protocols", "label": "Evaluation design & experiments", "definition": "Constructing offline splits and online experiments whose comparisons support the conclusions drawn from them.", "includes": [ "Holdout, temporal, and cross-validation splits.", "A/B tests, sequential monitoring, interference between units." ], "excludes": [ "Release staging mechanics ([[release-management]])" ], "contexts": [ "classical-ml", "interactive" ], "concepts": [ "mlsys.evaluation-split", "mlsys.selection-bias", "mlsys.online-experiment", "mlsys.cross-validation", "mlsys.sequential-testing", "mlsys.experiment-interference", "mlsys.evaluation-harness", "mlsys.paired-comparison" ], "related_concepts": [ "mlsys.leakage", "mlsys.shadow-deployment" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Lifecycle", "url": "https://mlsysbook.ai/vol1/03_ml_lifecycle/03_ml_lifecycle.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" }, { "volume": "vol4", "chapter": "Closed-Loop Evaluation", "url": "https://mlsysbook.ai/vol4/07_evaluation/07_evaluation.html" } ] }, { "code": "P3.2", "id": "metrics-uncertainty", "label": "Metrics, calibration & uncertainty", "definition": "Specifying what is measured and quantifying how far estimates and predicted probabilities can be trusted.", "includes": [ "Task metrics, calibration, confidence intervals, abstention.", "Distribution-free prediction sets." ], "excludes": [ "Choosing what to optimize ([[objectives-decisions]])" ], "contexts": [], "concepts": [ "mlsys.generalization", "mlsys.metrics", "mlsys.calibration", "mlsys.statistical-uncertainty", "mlsys.selective-prediction", "mlsys.conformal-prediction", "mlsys.ood-detection" ], "related_concepts": [ "mlsys.proxy-objective" ], "taught_in": [ { "volume": "vol1", "chapter": "Benchmarking", "url": "https://mlsysbook.ai/vol1/12_benchmarking/12_benchmarking.html" }, { "volume": "vol2", "chapter": "Robust AI", "url": "https://mlsysbook.ai/vol2/14_robust_ai/14_robust_ai.html" }, { "volume": "vol3", "chapter": "Agent Evaluation", "url": "https://mlsysbook.ai/vol3/13_evaluation/13_evaluation.html" }, { "volume": "vol4", "chapter": "Closed-Loop Evaluation", "url": "https://mlsysbook.ai/vol4/07_evaluation/07_evaluation.html" }, { "volume": "vol4", "chapter": "Grounded Intent", "url": "https://mlsysbook.ai/vol4/10_intent/10_intent.html" } ] }, { "code": "P3.3", "id": "robustness-slices", "label": "Sliced & robustness evaluation", "definition": "Measuring behavior separately across populations and conditions, and under deliberate shift or perturbation, rather than only in aggregate.", "includes": [ "Subgroup and condition slices, shift and perturbation tests." ], "excludes": [ "Fairness criteria themselves ([[fairness-bias]])", "Adversarial attack mechanics ([[security-privacy]])" ], "contexts": [], "concepts": [ "mlsys.sliced-evaluation", "mlsys.robustness-evaluation", "mlsys.slice-discovery" ], "related_concepts": [ "mlsys.fairness", "mlsys.distribution-shift", "mlsys.evasion" ], "taught_in": [ { "volume": "vol2", "chapter": "Robust AI", "url": "https://mlsysbook.ai/vol2/14_robust_ai/14_robust_ai.html" }, { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" }, { "volume": "vol4", "chapter": "Adversarial Verification", "url": "https://mlsysbook.ai/vol4/15_verification/15_verification.html" } ] }, { "code": "P3.4", "id": "generative-agent-eval", "label": "Evaluating generative & agentic systems", "definition": "Assessing open-ended outputs and multi-step runs where no single reference answer exists, using graders, sampling-aware metrics, and contamination controls.", "includes": [ "Model graders, pairwise and human preference, pass@k.", "Contamination of benchmarks by training data." ], "excludes": [ "Verifiers used to select outputs at inference ([[test-time-compute]])" ], "contexts": [ "generative", "agentic" ], "concepts": [ "mlsys.hallucination", "mlsys.agent-evaluation", "mlsys.llm-judge", "mlsys.pass-at-k", "mlsys.pairwise-preference-eval", "mlsys.human-evaluation", "mlsys.benchmark-contamination", "mlsys.dangerous-capability-eval", "mlsys.perplexity", "mlsys.reference-match-scoring" ], "related_concepts": [ "mlsys.verifier", "mlsys.statistical-uncertainty" ], "taught_in": [ { "volume": "vol3", "chapter": "Test-Time Compute", "url": "https://mlsysbook.ai/vol3/03_test_time_compute/03_test_time_compute.html" }, { "volume": "vol3", "chapter": "Agent Evaluation", "url": "https://mlsysbook.ai/vol3/13_evaluation/13_evaluation.html" } ] }, { "code": "P3.5", "id": "ml-testing", "label": "ML testing & debugging", "definition": "Testing data, models, pipelines, and their integration against stated expectations when exact expected outputs are often unknown.", "includes": [ "Oracles, metamorphic and behavioral tests, readiness rubrics." ], "excludes": [ "Fault injection into infrastructure ([[fault-injection]])" ], "contexts": [], "concepts": [ "mlsys.test-oracle", "mlsys.integration-testing", "mlsys.metamorphic-testing", "mlsys.behavioral-testing", "mlsys.production-readiness", "mlsys.model-visualization", "mlsys.error-analysis", "mlsys.training-diagnostics" ], "related_concepts": [ "mlsys.data-validation", "mlsys.failure-injection" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol4", "chapter": "Adversarial Verification", "url": "https://mlsysbook.ai/vol4/15_verification/15_verification.html" } ] } ] }, { "code": "P4", "id": "serving", "label": "Deployment & serving", "question": "How are trained models delivered to users within latency, throughput, and cost targets?", "scope": "Serving architectures, latency and goodput, request scheduling, LLM inference mechanisms, structured generation, test-time compute, retrieval, and on-device deployment.", "subjects": [ { "code": "P4.1", "id": "serving-architecture", "label": "Serving architectures", "definition": "The structure through which predictions are produced and delivered, from offline batch scoring to online and serverless model servers, multi-model and multi-adapter hosting, and streaming speech services.", "includes": [ "Batch vs online prediction, inference servers and APIs.", "Model composition, cascades, and cost/quality routing." ], "excludes": [ "Replica load balancing ([[request-scheduling]])", "Routing and composing calls across models ([[compound-ai]])", "Second-stage reranking inside retrieval ([[retrieval-rag]])" ], "contexts": [ "batch", "interactive", "streaming", "generative", "recommendation" ], "concepts": [ "mlsys.online-inference", "mlsys.batch-inference", "mlsys.model-server", "mlsys.multi-model-serving", "mlsys.ranking-funnel", "mlsys.multi-adapter-serving", "mlsys.keyed-predictions", "mlsys.stateless-serving-function", "mlsys.serverless-inference", "mlsys.streaming-asr", "mlsys.voice-pipeline", "mlsys.streaming-inference", "mlsys.inference-api" ], "related_concepts": [ "mlsys.retrieval", "mlsys.unit-cost", "mlsys.selective-prediction" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol3", "chapter": "Agent Economics", "url": "https://mlsysbook.ai/vol3/19_agent_economics/19_agent_economics.html" } ] }, { "code": "P4.2", "id": "compound-ai", "label": "Compound AI systems & model routing", "definition": "Composing several models, retrievers, and tools into one application, and routing, cascading, caching, and gating requests across models to meet quality and cost targets.", "includes": [ "Language-model programs and their optimization", "Model routing, cascades, gateways, and semantic caches" ], "excludes": [ "Single-model serving infrastructure ([[serving-architecture]])", "Agent loops that plan and act through tools ([[agent-harness]])" ], "contexts": [ "batch", "interactive", "streaming", "generative", "recommendation" ], "concepts": [ "mlsys.compound-ai-system", "mlsys.lm-program-optimization", "mlsys.model-routing", "mlsys.model-gateway", "mlsys.semantic-cache", "mlsys.model-cascade" ], "related_concepts": [ "mlsys.retrieval", "mlsys.unit-cost" ], "taught_in": [ { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol3", "chapter": "Agent Economics", "url": "https://mlsysbook.ai/vol3/19_agent_economics/19_agent_economics.html" } ] }, { "code": "P4.3", "id": "latency-goodput", "label": "Latency, throughput & goodput", "definition": "Defining and meeting per-request latency and useful-throughput targets, including tail behavior and token-level latency for generative services.", "includes": [ "Latency budgets, tail latency, TTFT and inter-token latency.", "Tail-tolerant request techniques." ], "excludes": [ "Analytic queueing models ([[queueing-models]])", "General measurement methodology and profiling ([[profiling-utilization]]); analytic queueing models ([[queueing-models]])" ], "contexts": [ "interactive", "generative" ], "concepts": [ "mlsys.tail-latency", "mlsys.slo-goodput", "mlsys.ttft", "mlsys.tpot", "mlsys.hedged-requests", "mlsys.request-throughput", "mlsys.latency-percentiles" ], "related_concepts": [ "mlsys.slo", "mlsys.queueing", "mlsys.latency-budget" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" } ] }, { "code": "P4.4", "id": "request-scheduling", "label": "Batching, admission & autoscaling", "definition": "Policies that group, admit, distribute, and provision for requests so a service meets its targets under varying load.", "includes": [ "Dynamic and iteration-level batching, admission, load balancing.", "Autoscaling and model cold start." ], "excludes": [ "Cluster-level job scheduling ([[cluster-scheduling]])" ], "contexts": [ "interactive", "generative", "distributed" ], "concepts": [ "mlsys.batching", "mlsys.admission", "mlsys.autoscaling", "mlsys.backpressure", "mlsys.continuous-batching", "mlsys.load-balancing", "mlsys.cold-start", "mlsys.llm-request-scheduling", "mlsys.cache-aware-routing" ], "related_concepts": [ "mlsys.service-capacity", "mlsys.capacity-planning" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" } ] }, { "code": "P4.5", "id": "llm-inference", "label": "LLM & generative inference", "definition": "Mechanisms specific to generative inference: managing attention state, separating prompt processing from token generation, accelerating autoregressive decoding, and reducing iterative denoising cost.", "includes": [ "KV cache management, prefix reuse, speculative decoding.", "Chunked prefill and prefill/decode disaggregation.", "Denoising-step reduction and feature caching for diffusion models." ], "excludes": [ "Weight quantization ([[model-compression]])", "Context selection and compaction ([[context-memory]])" ], "contexts": [ "generative", "interactive", "distributed", "multimodal" ], "concepts": [ "mlsys.kv-cache", "mlsys.prefill-decode", "mlsys.paged-kv", "mlsys.prefix-cache", "mlsys.speculative-decoding", "mlsys.chunked-prefill", "mlsys.pd-disaggregation", "mlsys.encoder-disaggregation", "mlsys.kv-cache-offload", "mlsys.kv-cache-transfer", "mlsys.kv-cache-eviction", "mlsys.cpu-gpu-hybrid-inference", "mlsys.non-prefix-kv-reuse", "mlsys.token-streaming", "mlsys.denoising-step-reduction", "mlsys.diffusion-feature-caching" ], "related_concepts": [ "mlsys.continuous-batching", "mlsys.quantization", "mlsys.tensor-parallel", "mlsys.attention" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol3", "chapter": "KV Cache Management", "url": "https://mlsysbook.ai/vol3/05_kv_cache/05_kv_cache.html" } ] }, { "code": "P4.6", "id": "structured-generation", "label": "Decoding, sampling & structured output", "definition": "Controlling how tokens are selected and constraining generated output to required syntax or schemas.", "includes": [ "Temperature and nucleus sampling, grammar-constrained decoding, schema checks." ], "excludes": [ "Tool authority and effects ([[tool-interfaces]])" ], "contexts": [ "generative", "agentic" ], "concepts": [ "mlsys.grammar", "mlsys.sampling-controls", "mlsys.constrained-decoding", "mlsys.output-validation" ], "related_concepts": [ "mlsys.tool-contract", "mlsys.hallucination" ], "taught_in": [ { "volume": "vol3", "chapter": "The Foundation Model", "url": "https://mlsysbook.ai/vol3/02_foundation_model/02_foundation_model.html" } ] }, { "code": "P4.7", "id": "test-time-compute", "label": "Test-time compute", "definition": "Spending additional inference computation on reasoning, sampling, search, and verification to raise output quality under a budget.", "includes": [ "Reasoning budgets, parallel sampling and selection, verifiers, tree search.", "Trading inference compute against model size." ], "excludes": [ "Agent control loops and tool use ([[agent-harness]])", "Pass@k as an evaluation metric ([[generative-agent-eval]])" ], "contexts": [ "generative", "agentic" ], "concepts": [ "mlsys.reasoning-budget", "mlsys.best-of-n", "mlsys.self-consistency", "mlsys.verifier", "mlsys.process-reward-model", "mlsys.inference-time-search", "mlsys.test-time-scaling" ], "related_concepts": [ "mlsys.pass-at-k", "mlsys.reward-model", "mlsys.planning", "mlsys.unit-cost" ], "taught_in": [ { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol3", "chapter": "Test-Time Compute", "url": "https://mlsysbook.ai/vol3/03_test_time_compute/03_test_time_compute.html" } ] }, { "code": "P4.8", "id": "retrieval-rag", "label": "Retrieval & vector search", "definition": "Selecting relevant records from an external corpus by lexical, dense, or hybrid search and supplying them to a model at inference.", "includes": [ "Retrieval-augmented generation, ANN indexes and vector stores, reranking." ], "excludes": [ "Agent memory write and consolidation policy ([[context-memory]])" ], "contexts": [ "generative", "agentic", "interactive" ], "concepts": [ "mlsys.retrieval", "mlsys.ann", "mlsys.rag", "mlsys.dense-retrieval", "mlsys.lexical-retrieval", "mlsys.reranking", "mlsys.graph-vector-index", "mlsys.quantized-vector-index", "mlsys.filtered-vector-search", "mlsys.index-freshness", "mlsys.multi-hop-retrieval" ], "related_concepts": [ "mlsys.embeddings", "mlsys.context" ], "taught_in": [ { "volume": "vol2", "chapter": "Data Storage", "url": "https://mlsysbook.ai/vol2/04_data_storage/04_data_storage.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol3", "chapter": "Long-Term Memory", "url": "https://mlsysbook.ai/vol3/06_long_term_memory/06_long_term_memory.html" } ] }, { "code": "P4.9", "id": "on-device-deployment", "label": "On-device & edge deployment", "definition": "Converting, packaging, executing, and updating models on phones, embedded devices, and microcontrollers under memory, energy, and connectivity limits.", "includes": [ "Export and conversion, on-device runtimes, device/cloud split.", "Over-the-air model updates across heterogeneous fleets." ], "excludes": [ "Edge silicon ([[edge-embedded-hardware]])", "Compression techniques ([[model-compression]])" ], "contexts": [ "edge", "physical" ], "concepts": [ "mlsys.model-conversion", "mlsys.on-device-runtime", "mlsys.ota-model-update", "mlsys.device-cloud-partitioning", "mlsys.device-heterogeneity", "mlsys.ui-thread-inference" ], "related_concepts": [ "mlsys.quantization", "mlsys.placement", "mlsys.on-device-training", "mlsys.memory-capacity" ], "taught_in": [ { "volume": "vol1", "chapter": "System Paradigms", "url": "https://mlsysbook.ai/vol1/02_system_paradigms/02_system_paradigms.html" }, { "volume": "vol1", "chapter": "Model Compression", "url": "https://mlsysbook.ai/vol1/10_model_compression/10_model_compression.html" }, { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol2", "chapter": "Edge Intelligence", "url": "https://mlsysbook.ai/vol2/11_edge_intelligence/11_edge_intelligence.html" }, { "volume": "vol4", "chapter": "The Cognitive Brain", "url": "https://mlsysbook.ai/vol4/03_brain/03_brain.html" } ] } ] }, { "code": "P5", "id": "operations", "label": "MLOps & operations", "question": "How is a deployed ML system versioned, observed, changed, and eventually retired?", "scope": "Release management, monitoring, drift and retraining, incident response, pipelines and reproducibility, platforms and teams, and retirement.", "subjects": [ { "code": "P5.1", "id": "release-management", "label": "Versioning, rollout & rollback", "definition": "Identifying, gating, and progressively exposing model releases, with the ability to return to a known-good configuration.", "includes": [ "Registries, CI/CD for models, release gates.", "Shadow, canary, and staged rollout; rollback." ], "excludes": [ "Statistical design of experiments ([[evaluation-protocols]])" ], "contexts": [], "concepts": [ "mlsys.artifact-version", "mlsys.rollout", "mlsys.rollback", "mlsys.model-registry", "mlsys.shadow-deployment", "mlsys.ml-ci-cd", "mlsys.release-gate", "mlsys.model-feature-compatibility" ], "related_concepts": [ "mlsys.online-experiment", "mlsys.model-artifact", "mlsys.ota-model-update" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" }, { "volume": "vol3", "chapter": "Operating Agents", "url": "https://mlsysbook.ai/vol3/14_agent_operations/14_agent_operations.html" }, { "volume": "vol4", "chapter": "Deployment Release", "url": "https://mlsysbook.ai/vol4/16_release/16_release.html" } ] }, { "code": "P5.2", "id": "monitoring", "label": "Monitoring & observability", "definition": "Instrumenting deployed systems and detecting, from logged signals, changes in inputs, outputs, quality, and health.", "includes": [ "Prediction logging, drift statistics, alerting and alert fatigue.", "Metrics, logs, traces for model services and agents." ], "excludes": [ "Responding to detected incidents ([[incident-response]])" ], "contexts": [], "concepts": [ "mlsys.monitoring", "mlsys.observability", "mlsys.drift-detection", "mlsys.inference-logging", "mlsys.alerting", "mlsys.distributed-tracing", "mlsys.llm-call-tracing" ], "related_concepts": [ "mlsys.distribution-shift", "mlsys.delayed-outcomes", "mlsys.privacy-leakage" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol2", "chapter": "Fault Tolerance", "url": "https://mlsysbook.ai/vol2/07_fault_tolerance/07_fault_tolerance.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" }, { "volume": "vol3", "chapter": "Operating Agents", "url": "https://mlsysbook.ai/vol3/14_agent_operations/14_agent_operations.html" } ] }, { "code": "P5.3", "id": "drift-retraining", "label": "Drift, feedback loops & retraining", "definition": "Understanding how deployment data and outcomes change, including changes the system itself causes, and deciding when and how to update the model.", "includes": [ "Covariate and concept drift, delayed labels, hidden feedback loops.", "Scheduled and triggered retraining, continuous training." ], "excludes": [ "Incremental learning algorithms ([[continual-learning]])" ], "contexts": [ "classical-ml", "streaming" ], "concepts": [ "mlsys.distribution-shift", "mlsys.concept-drift", "mlsys.delayed-outcomes", "mlsys.feedback-loop", "mlsys.retraining", "mlsys.continuous-training", "mlsys.user-feedback", "mlsys.fleet-data-engine" ], "related_concepts": [ "mlsys.online-learning", "mlsys.drift-detection" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol2", "chapter": "Robust AI", "url": "https://mlsysbook.ai/vol2/14_robust_ai/14_robust_ai.html" }, { "volume": "vol4", "chapter": "Physical Data", "url": "https://mlsysbook.ai/vol4/05_data/05_data.html" } ] }, { "code": "P5.4", "id": "incident-response", "label": "Incident response", "definition": "Detecting, triaging, mitigating, and learning from production failures, including attributing them to data, model, or infrastructure causes.", "includes": [ "Triage, mitigation, blameless postmortems, on-call." ], "excludes": [ "Failure semantics and recovery mechanisms ([[reliability]])" ], "contexts": [], "concepts": [ "mlsys.incident-response", "mlsys.postmortem", "mlsys.failure-attribution" ], "related_concepts": [ "mlsys.rollback", "mlsys.observability", "mlsys.failure-model" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" }, { "volume": "vol3", "chapter": "Operating Agents", "url": "https://mlsysbook.ai/vol3/14_agent_operations/14_agent_operations.html" } ] }, { "code": "P5.5", "id": "pipelines-reproducibility", "label": "ML pipelines & reproducibility", "definition": "Automating the steps that produce models and controlling their environments and configuration so runs can be repeated.", "includes": [ "Orchestrated pipelines, environment pinning, configuration as code." ], "excludes": [ "Experiment search strategy ([[experiment-management]])", "Scheduling containers across a cluster ([[cluster-scheduling]])" ], "contexts": [ "batch" ], "concepts": [ "mlsys.dependency-environment", "mlsys.workflow-orchestration", "mlsys.experiment-reproducibility", "mlsys.configuration-management", "mlsys.infrastructure-as-code", "mlsys.containerization" ], "related_concepts": [ "mlsys.lineage", "mlsys.experiment-tracking" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Lifecycle", "url": "https://mlsysbook.ai/vol1/03_ml_lifecycle/03_ml_lifecycle.html" }, { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" } ] }, { "code": "P5.6", "id": "ml-platforms", "label": "ML platforms, teams & technical debt", "definition": "Shared internal platforms, team structures, and debt management that let an organization operate many models at sustainable cost.", "includes": [ "Platform-as-product, maturity levels, ownership and roles.", "ML-specific technical debt and anti-patterns." ], "excludes": [ "Fleet hardware and scheduling ([[infrastructure-fleet]])", "Governance and regulation ([[governance-regulation]])" ], "contexts": [], "concepts": [ "mlsys.ml-platform", "mlsys.technical-debt", "mlsys.entanglement", "mlsys.undeclared-consumers", "mlsys.mlops-maturity", "mlsys.ml-roles", "mlsys.glue-code", "mlsys.pipeline-jungle", "mlsys.build-vs-buy" ], "related_concepts": [ "mlsys.feature-store", "mlsys.model-registry", "mlsys.data-dependency" ], "taught_in": [ { "volume": "vol1", "chapter": "ML Operations", "url": "https://mlsysbook.ai/vol1/14_ml_ops/14_ml_ops.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" } ] }, { "code": "P5.7", "id": "retirement", "label": "Model retirement", "definition": "Ending a model's service by migrating its consumers, archiving what must be kept, and deleting data and resources that are no longer justified.", "includes": [ "Deprecation with consumer migration, archival for audit, retention and deletion." ], "excludes": [ "Removing learned influence of specific records ([[privacy-dp]])" ], "contexts": [], "concepts": [ "mlsys.model-deprecation", "mlsys.artifact-archival", "mlsys.data-retention", "mlsys.deletion-propagation" ], "related_concepts": [ "mlsys.undeclared-consumers", "mlsys.privacy", "mlsys.lineage" ], "taught_in": [ { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" } ] } ] } ] }, { "code": "Q", "id": "qualities", "label": "Qualities", "question": "By which properties is the system judged, and how are they engineered?", "areas": [ { "code": "Q1", "id": "efficiency", "label": "Performance, efficiency & benchmarking", "question": "How do we measure, compare, and reduce the time, memory, and energy a workload consumes?", "scope": "Measuring and benchmarking where time, memory, and energy go, and the cross-layer techniques whose primary purpose is to reduce them, such as compression and hardware-aware design.", "subjects": [ { "code": "Q1.1", "id": "profiling-utilization", "label": "Profiling & utilization", "definition": "Measuring where execution time and resources go under a stated workload, and expressing how much of the hardware's capacity does useful model work.", "includes": [ "Profilers and execution-time breakdowns", "Utilization metrics such as MFU and training goodput", "Bottleneck attribution across compute, memory, I/O, and communication" ], "excludes": [ "Standardized benchmark suites and reporting rules ([[system-benchmarking]])", "Worst-case execution-time bounds ([[real-time-systems]])", "Serving-specific latency and goodput targets ([[latency-goodput]])" ], "contexts": [ "deep-learning", "distributed" ], "concepts": [ "mlsys.profiling", "mlsys.execution-time", "mlsys.utilization", "mlsys.model-flops-utilization", "mlsys.bottleneck-analysis", "mlsys.training-goodput" ], "related_concepts": [ "mlsys.roofline", "mlsys.benchmark", "mlsys.slo-goodput" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Training", "url": "https://mlsysbook.ai/vol1/08_training/08_training.html" }, { "volume": "vol1", "chapter": "Benchmarking", "url": "https://mlsysbook.ai/vol1/12_benchmarking/12_benchmarking.html" }, { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol2", "chapter": "Fleet Efficiency", "url": "https://mlsysbook.ai/vol2/09_performance_engineering/09_performance_engineering.html" } ] }, { "code": "Q1.2", "id": "system-benchmarking", "label": "System benchmarking", "definition": "Measuring training and inference system performance under specified workloads, quality targets, load scenarios, and run rules so results are comparable.", "includes": [ "MLPerf-style time-to-quality and scenario-based inference metrics." ], "excludes": [ "Profiling a single workload ([[profiling-utilization]])" ], "contexts": [ "distributed", "edge" ], "concepts": [ "mlsys.benchmark", "mlsys.time-to-train", "mlsys.inference-load-scenario", "mlsys.benchmark-representativeness", "mlsys.performance-regression-testing", "mlsys.measurement-variability" ], "related_concepts": [ "mlsys.profiling", "mlsys.energy", "mlsys.slo-goodput", "mlsys.statistical-uncertainty" ], "taught_in": [ { "volume": "vol1", "chapter": "Benchmarking", "url": "https://mlsysbook.ai/vol1/12_benchmarking/12_benchmarking.html" }, { "volume": "vol2", "chapter": "Fleet Efficiency", "url": "https://mlsysbook.ai/vol2/09_performance_engineering/09_performance_engineering.html" } ] }, { "code": "Q1.3", "id": "model-compression", "label": "Model compression", "definition": "Techniques that shrink a trained or training model's parameters, precision, or rank to reduce memory, compute, and latency at a measured cost in quality.", "includes": [ "Quantization (post-training and quantization-aware)", "Pruning (structured and unstructured) and sparsity exploitation", "Knowledge distillation and low-rank factorization" ], "excludes": [ "Numeric format definitions and mixed-precision training ([[numerics]])", "Parameter-efficient fine-tuning such as LoRA, whose purpose is adaptation ([[fine-tuning]])" ], "contexts": [ "deep-learning", "generative", "edge" ], "concepts": [ "mlsys.quantization", "mlsys.sparsity", "mlsys.distillation", "mlsys.pruning", "mlsys.structured-pruning", "mlsys.low-rank-factorization", "mlsys.quantization-aware-training", "mlsys.post-training-quantization", "mlsys.weight-only-quantization", "mlsys.activation-outliers", "mlsys.kv-cache-quantization", "mlsys.nm-sparsity", "mlsys.activation-sparsity" ], "related_concepts": [ "mlsys.precision", "mlsys.mixed-precision" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Compression", "url": "https://mlsysbook.ai/vol1/10_model_compression/10_model_compression.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" } ] }, { "code": "Q1.4", "id": "hardware-aware-design", "label": "Hardware-aware model design", "definition": "Choosing or searching model architectures against a target device's measured cost, so that quality and efficiency are optimized together rather than compressed after the fact.", "includes": [ "Neural architecture search, including hardware-aware objectives", "Efficient building blocks and the accuracy-efficiency frontier", "Joint model and hardware co-design" ], "excludes": [ "Mixture-of-experts and early exit ([[conditional-computation]])", "Efficient attention and state-space models ([[efficient-sequence-models]])" ], "contexts": [ "deep-learning", "edge" ], "concepts": [ "mlsys.neural-architecture-search", "mlsys.hardware-aware-nas", "mlsys.efficiency-frontier", "mlsys.model-hardware-co-design" ], "related_concepts": [ "mlsys.accelerator-execution-architecture", "mlsys.arithmetic-intensity", "mlsys.depthwise-separable-convolution" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Compression", "url": "https://mlsysbook.ai/vol1/10_model_compression/10_model_compression.html" } ] } ] }, { "code": "Q2", "id": "reliability", "label": "Reliability & fault tolerance", "question": "How does the system keep working, or fail safely, when components fail at scale?", "scope": "Infrastructure and service fault tolerance, from failure assumptions and recovery semantics to fleet failure rates, checkpoint economics, and resilience testing.", "subjects": [ { "code": "Q2.1", "id": "failure-semantics", "label": "Failure models, retries & degradation", "definition": "Declared failure assumptions and the service-level mechanisms (retries, idempotency, replication, circuit breaking, degradation) that keep requests correct and available when components fail.", "includes": [ "Failure models and recovery semantics (idempotency, compensation)", "Retries, circuit breakers, replication, and failover", "Graceful degradation of quality or features under failure" ], "excludes": [ "Physical safety fallbacks and stopping envelopes ([[runtime-enforcement]])", "Durable agent execution and replay ([[durable-execution]])" ], "contexts": [ "interactive", "distributed", "agentic" ], "concepts": [ "mlsys.failure-model", "mlsys.idempotency", "mlsys.compensation", "mlsys.retry-backoff", "mlsys.circuit-breaker", "mlsys.replication-failover", "mlsys.graceful-degradation", "mlsys.health-checks" ], "related_concepts": [ "mlsys.backpressure", "mlsys.admission", "mlsys.runtime-assurance" ], "taught_in": [ { "volume": "vol2", "chapter": "Fault Tolerance", "url": "https://mlsysbook.ai/vol2/07_fault_tolerance/07_fault_tolerance.html" }, { "volume": "vol2", "chapter": "Serving at Scale", "url": "https://mlsysbook.ai/vol2/10_inference/10_inference.html" }, { "volume": "vol3", "chapter": "Failure Recovery", "url": "https://mlsysbook.ai/vol3/12_failure_recovery/12_failure_recovery.html" } ] }, { "code": "Q2.2", "id": "checkpoint-recovery", "label": "Checkpointing & elastic recovery", "definition": "Persisting recoverable state for long-running jobs and choosing checkpoint frequency and recovery strategy to minimize lost work and restart cost under a given failure rate.", "includes": [ "Checkpoints and consistent cuts for distributed state", "Optimal checkpoint interval (Young-Daly) and asynchronous checkpointing", "Elastic recovery on a changed set of workers" ], "excludes": [ "Activation recomputation, sometimes called gradient checkpointing ([[training-memory]])", "Checkpoint storage bandwidth and I/O systems ([[storage-systems]])" ], "contexts": [ "distributed", "deep-learning" ], "concepts": [ "mlsys.checkpoint", "mlsys.consistent-cut", "mlsys.checkpoint-interval", "mlsys.async-checkpointing", "mlsys.checkpoint-resharding" ], "related_concepts": [ "mlsys.training-state", "mlsys.training-goodput", "mlsys.elastic-training" ], "taught_in": [ { "volume": "vol2", "chapter": "Data Storage", "url": "https://mlsysbook.ai/vol2/04_data_storage/04_data_storage.html" }, { "volume": "vol2", "chapter": "Fault Tolerance", "url": "https://mlsysbook.ai/vol2/07_fault_tolerance/07_fault_tolerance.html" } ] }, { "code": "Q2.3", "id": "hardware-faults", "label": "Hardware faults & silent data corruption", "definition": "How hardware fails in a fleet, from fail-stop crashes to gray and silent faults, how often it fails, and how such faults are detected and contained.", "includes": [ "Fail-stop, gray, and silent failures and their detection", "Fleet failure rates (MTBF composition) and availability", "Correlated failures and failure domains" ], "excludes": [ "Distribution shift and adversarial robustness ([[drift-retraining]]; [[ml-attacks]])", "Chip power and thermal limits as design constraints ([[chip-power-thermal]])" ], "contexts": [ "distributed" ], "concepts": [ "mlsys.silent-data-corruption", "mlsys.mtbf", "mlsys.availability", "mlsys.gray-failure", "mlsys.failure-domain", "mlsys.abft", "mlsys.distributed-hang-diagnosis", "mlsys.flash-endurance" ], "related_concepts": [ "mlsys.stragglers", "mlsys.thermal-dynamics" ], "taught_in": [ { "volume": "vol2", "chapter": "Fault Tolerance", "url": "https://mlsysbook.ai/vol2/07_fault_tolerance/07_fault_tolerance.html" }, { "volume": "vol2", "chapter": "Robust AI", "url": "https://mlsysbook.ai/vol2/14_robust_ai/14_robust_ai.html" } ] }, { "code": "Q2.4", "id": "fault-injection", "label": "Fault injection & resilience testing", "definition": "Deliberately introducing software, infrastructure, or hardware faults under controlled scope to verify that detection, recovery, and degradation work as designed.", "includes": [ "Chaos engineering and failure injection in production-like settings", "Game days and recovery rehearsals", "Hardware fault injection to estimate model error resilience" ], "excludes": [ "Functional ML tests and test oracles ([[ml-testing]])", "Scenario-based safety validation ([[verification-validation]])" ], "contexts": [ "distributed", "interactive", "physical" ], "concepts": [ "mlsys.failure-injection", "mlsys.chaos-engineering", "mlsys.game-day", "mlsys.hardware-fault-injection", "mlsys.blast-radius" ], "related_concepts": [ "mlsys.incident-response", "mlsys.integration-testing" ], "taught_in": [ { "volume": "vol2", "chapter": "Fault Tolerance", "url": "https://mlsysbook.ai/vol2/07_fault_tolerance/07_fault_tolerance.html" }, { "volume": "vol4", "chapter": "Adversarial Verification", "url": "https://mlsysbook.ai/vol4/15_verification/15_verification.html" } ] } ] }, { "code": "Q3", "id": "security-privacy", "label": "Security & privacy", "question": "How do we protect ML systems and the data they touch from adversaries and leakage?", "scope": "Adversaries and defenses across data, model, artifact, and execution boundaries, and the leakage of information about people or records through learned systems.", "subjects": [ { "code": "Q3.1", "id": "ml-attacks", "label": "Threat models & adversarial attacks", "definition": "Specifying adversaries against learned systems and the attacks they mount on training data, model inputs, and instruction boundaries, with the defenses aimed at each.", "includes": [ "Threat models for ML assets and interfaces", "Poisoning, backdoors, evasion, jailbreaks, and prompt injection", "Defenses such as adversarial training" ], "excludes": [ "Robustness evaluation methodology ([[robustness-slices]])", "Theft and integrity of model artifacts ([[model-supply-chain]])" ], "contexts": [ "deep-learning", "generative", "agentic" ], "concepts": [ "mlsys.threat-model", "mlsys.poisoning", "mlsys.evasion", "mlsys.prompt-injection", "mlsys.backdoor", "mlsys.jailbreak", "mlsys.adversarial-training", "mlsys.red-teaming", "mlsys.byzantine-robust-aggregation", "mlsys.sensor-spoofing", "mlsys.randomized-smoothing" ], "related_concepts": [ "mlsys.robustness-evaluation" ], "taught_in": [ { "volume": "vol2", "chapter": "Security & Privacy", "url": "https://mlsysbook.ai/vol2/13_security_privacy/13_security_privacy.html" }, { "volume": "vol2", "chapter": "Robust AI", "url": "https://mlsysbook.ai/vol2/14_robust_ai/14_robust_ai.html" }, { "volume": "vol3", "chapter": "Agent Security", "url": "https://mlsysbook.ai/vol3/09_security/09_security.html" } ] }, { "code": "Q3.2", "id": "model-supply-chain", "label": "Model & supply-chain security", "definition": "Protecting models as valuable artifacts from theft and tampering, and protecting the chain of datasets, pretrained weights, libraries, and builds that produce them.", "includes": [ "Model extraction and ownership evidence (watermarking)", "Artifact signing and provenance", "Malicious weights, serialization formats, and dependencies" ], "excludes": [ "Artifact versioning and registries as release tooling ([[release-management]])", "Data lineage as a data-engineering practice ([[data-lineage]])" ], "contexts": [ "deep-learning", "generative" ], "concepts": [ "mlsys.model-extraction", "mlsys.model-watermarking", "mlsys.artifact-signing", "mlsys.unsafe-deserialization", "mlsys.supply-chain-compromise" ], "related_concepts": [ "mlsys.model-artifact", "mlsys.lineage", "mlsys.dependency-environment" ], "taught_in": [ { "volume": "vol2", "chapter": "Security & Privacy", "url": "https://mlsysbook.ai/vol2/13_security_privacy/13_security_privacy.html" } ] }, { "code": "Q3.3", "id": "privacy-dp", "label": "Privacy leakage & differential privacy", "definition": "Privacy requirements for data about people, the attacks that extract it from models and outputs, and formal or architectural mechanisms that bound or remove that leakage.", "includes": [ "Membership inference and model inversion", "Differential privacy and DP-SGD", "Machine unlearning and secure aggregation" ], "excludes": [ "Federated training protocols and client scheduling ([[federated-learning]])", "Privacy regulation and governance processes ([[governance-regulation]])" ], "contexts": [ "federated", "generative", "edge" ], "concepts": [ "mlsys.privacy", "mlsys.differential-privacy", "mlsys.privacy-leakage", "mlsys.membership-inference", "mlsys.model-inversion", "mlsys.dp-sgd", "mlsys.machine-unlearning", "mlsys.secure-aggregation", "mlsys.he-inference", "mlsys.secure-computation-inference" ], "related_concepts": [ "mlsys.federated-learning" ], "taught_in": [ { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol2", "chapter": "Edge Intelligence", "url": "https://mlsysbook.ai/vol2/11_edge_intelligence/11_edge_intelligence.html" }, { "volume": "vol2", "chapter": "Security & Privacy", "url": "https://mlsysbook.ai/vol2/13_security_privacy/13_security_privacy.html" }, { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" } ] }, { "code": "Q3.4", "id": "access-isolation", "label": "Access control & isolation", "definition": "Constraining who may act, what code may reach, and which parties can observe data in use, through authorization, least privilege, isolation, and hardware-rooted trust.", "includes": [ "Authorization, least privilege, and sandboxed execution", "Confidential computing and remote attestation", "Side channels across shared accelerators and caches" ], "excludes": [ "Tenant quotas and resource isolation for performance ([[capacity-provisioning]])", "Agent tool contracts and action approval ([[tool-interfaces]])" ], "contexts": [ "agentic", "distributed" ], "concepts": [ "mlsys.authorization", "mlsys.isolation", "mlsys.least-privilege", "mlsys.confidential-computing", "mlsys.remote-attestation", "mlsys.side-channel", "mlsys.capability-based-security", "mlsys.ephemeral-sandbox", "mlsys.egress-control", "mlsys.information-flow-control", "mlsys.secrets-management" ], "related_concepts": [ "mlsys.tool-contract" ], "taught_in": [ { "volume": "vol2", "chapter": "Security & Privacy", "url": "https://mlsysbook.ai/vol2/13_security_privacy/13_security_privacy.html" }, { "volume": "vol3", "chapter": "Agent Sandboxes", "url": "https://mlsysbook.ai/vol3/08_sandboxes/08_sandboxes.html" }, { "volume": "vol3", "chapter": "Agent Security", "url": "https://mlsysbook.ai/vol3/09_security/09_security.html" } ] } ] }, { "code": "Q4", "id": "safety-assurance", "label": "Safety & assurance", "question": "How do we argue, enforce, and verify that a system will not cause unacceptable harm?", "scope": "Identifying hazards, bounding the operating domain, enforcing safety at runtime, verifying and validating behavior, and arguing acceptable residual risk against recognized standards.", "subjects": [ { "code": "Q4.1", "id": "hazards-operating-domain", "label": "Hazard analysis & operating domains", "definition": "Identifying how a system can contribute to harm, including hazards from functional insufficiency rather than faults, and fixing the operating conditions within which safety claims are made.", "includes": [ "Hazard identification and analysis (STPA, FMEA, fault trees) applied to learned components.", "Operational design domains and triggering conditions for functional insufficiency (SOTIF)." ], "excludes": [ "Adversary-driven threat modeling ([[ml-attacks]])", "Infrastructure fault models ([[failure-semantics]])" ], "contexts": [ "physical", "agentic", "deep-learning" ], "concepts": [ "mlsys.operating-domain", "mlsys.hazard-analysis", "mlsys.stpa", "mlsys.fmea", "mlsys.functional-insufficiency" ], "related_concepts": [ "mlsys.failure-model", "mlsys.threat-model", "mlsys.distribution-shift" ], "taught_in": [ { "volume": "vol4", "chapter": "The Causal Boundary", "url": "https://mlsysbook.ai/vol4/01_boundary/01_boundary.html" }, { "volume": "vol4", "chapter": "Physical Data", "url": "https://mlsysbook.ai/vol4/05_data/05_data.html" }, { "volume": "vol4", "chapter": "Adversarial Verification", "url": "https://mlsysbook.ai/vol4/15_verification/15_verification.html" } ] }, { "code": "Q4.2", "id": "runtime-enforcement", "label": "Runtime assurance & safety filters", "definition": "Mechanisms that monitor a learned component at run time and override, constrain, or replace its outputs so the system stays within a verified safe set.", "includes": [ "Safety filters, control barrier functions, Simplex switching, and fallback to a minimal-risk condition.", "Input and output guardrails that block or rewrite unsafe generated content or actions." ], "excludes": [ "Human approval of agent actions ([[agent-harness]])", "Retry, failover, and graceful degradation for availability ([[reliability]])" ], "contexts": [ "physical", "agentic", "generative", "interactive" ], "concepts": [ "mlsys.runtime-assurance", "mlsys.stopping-envelope", "mlsys.safety-filter", "mlsys.control-barrier-function", "mlsys.simplex-architecture", "mlsys.minimal-risk-condition", "mlsys.guardrails" ], "related_concepts": [ "mlsys.trajectory-feasibility", "mlsys.selective-prediction", "mlsys.approval-gate" ], "taught_in": [ { "volume": "vol4", "chapter": "Safety Enforcement", "url": "https://mlsysbook.ai/vol4/12_enforcement/12_enforcement.html" }, { "volume": "vol4", "chapter": "Workload Placement", "url": "https://mlsysbook.ai/vol4/13_placement/13_placement.html" }, { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" } ] }, { "code": "Q4.3", "id": "verification-validation", "label": "Safety verification & validation", "definition": "Producing evidence that a system meets its safety requirements across its operating domain, through scenario testing, falsification, formal proof, and statistical validation of rare failures.", "includes": [ "Scenario coverage, simulation-based falsification, and software/hardware-in-the-loop qualification.", "Formal and statistical guarantees, including verification of neural networks and rare-event estimation." ], "excludes": [ "Average-case model metrics and benchmarks ([[evaluation]])" ], "contexts": [ "physical", "deep-learning" ], "concepts": [ "mlsys.scenario-based-testing", "mlsys.falsification", "mlsys.formal-verification", "mlsys.neural-network-verification", "mlsys.rare-event-validation", "mlsys.xil-testing", "mlsys.closed-loop-evaluation" ], "related_concepts": [ "mlsys.robustness-evaluation", "mlsys.failure-injection", "mlsys.sim-to-real", "mlsys.test-oracle" ], "taught_in": [ { "volume": "vol4", "chapter": "Closed-Loop Evaluation", "url": "https://mlsysbook.ai/vol4/07_evaluation/07_evaluation.html" }, { "volume": "vol4", "chapter": "Adversarial Verification", "url": "https://mlsysbook.ai/vol4/15_verification/15_verification.html" } ] }, { "code": "Q4.4", "id": "safety-cases", "label": "Safety cases, standards & certification", "definition": "Structured arguments that a system is acceptably safe for a defined use, the standards that set what evidence is required, and the field indicators that keep the argument valid after release.", "includes": [ "Safety-case structure and notation (claims, arguments, evidence; GSN).", "Functional-safety and autonomy standards (ISO 26262, IEC 61508, ISO 21448, UL 4600) and integrity levels." ], "excludes": [ "Regulatory compliance not specific to safety, such as the EU AI Act ([[governance-regulation]])" ], "contexts": [ "physical", "agentic" ], "concepts": [ "mlsys.safety-case", "mlsys.goal-structuring-notation", "mlsys.functional-safety", "mlsys.safety-integrity-level", "mlsys.safety-performance-indicator", "mlsys.event-data-recorder", "mlsys.change-impact-analysis" ], "related_concepts": [ "mlsys.operating-domain", "mlsys.rollout", "mlsys.artifact-version" ], "taught_in": [ { "volume": "vol4", "chapter": "Deployment Release", "url": "https://mlsysbook.ai/vol4/16_release/16_release.html" }, { "volume": "vol3", "chapter": "Operating Agents", "url": "https://mlsysbook.ai/vol3/14_agent_operations/14_agent_operations.html" }, { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" } ] } ] }, { "code": "Q5", "id": "responsible-ai", "label": "Responsible AI", "question": "How do we ensure the system's outcomes are fair, accountable, transparent, and governed?", "scope": "Measuring and mitigating unfair outcomes, explaining and documenting models and data, keeping people able to oversee and contest decisions, and governing AI risk under organizational and legal regimes.", "subjects": [ { "code": "Q5.1", "id": "fairness-bias", "label": "Fairness & bias", "definition": "Specifying, measuring, and mitigating how a system's outcomes and errors differ across people and groups.", "includes": [ "Group and individual fairness criteria and the metrics that operationalize them.", "Pre-, in-, and post-processing mitigation and its accuracy cost." ], "excludes": [ "Per-slice measurement machinery in general ([[robustness-slices]])", "Scheduler fair sharing ([[cluster-scheduling]])" ], "contexts": [ "classical-ml", "deep-learning", "generative" ], "concepts": [ "mlsys.fairness", "mlsys.group-fairness-metric", "mlsys.individual-fairness", "mlsys.fairness-incompatibility", "mlsys.bias-mitigation" ], "related_concepts": [ "mlsys.sliced-evaluation", "mlsys.feedback-loop", "mlsys.proxy-objective", "mlsys.data-acquisition" ], "taught_in": [ { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" }, { "volume": "vol4", "chapter": "Responsible Physical AI", "url": "https://mlsysbook.ai/vol4/17_responsibility/17_responsibility.html" } ] }, { "code": "Q5.2", "id": "transparency-explainability", "label": "Explainability & documentation", "definition": "Making model behavior understandable to the people who build, deploy, and are affected by it, through explanations, interpretable models, and standardized documentation of models and data.", "includes": [ "Post-hoc explanations (feature attribution, counterfactuals) and inherently interpretable models.", "Model cards, datasheets, and the serving cost of producing explanations." ], "excludes": [ "Debugging-oriented model inspection ([[ml-testing]])", "Data provenance records ([[data-lineage]])" ], "contexts": [ "classical-ml", "deep-learning", "generative" ], "concepts": [ "mlsys.feature-attribution", "mlsys.counterfactual-explanation", "mlsys.interpretable-model", "mlsys.model-card", "mlsys.datasheet" ], "related_concepts": [ "mlsys.lineage", "mlsys.calibration" ], "taught_in": [ { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" } ] }, { "code": "Q5.3", "id": "human-oversight", "label": "Human oversight & contestability", "definition": "Arrangements that let people supervise, intervene in, take over from, and challenge consequential system decisions, and the human-factors limits on doing so.", "includes": [ "Human-in-the-loop and on-the-loop control, recourse and appeal, and control handover.", "Automation bias, complacency, and the ironies of automation." ], "excludes": [ "The mechanics of agent approval gates ([[agent-harness]])" ], "contexts": [ "classical-ml", "agentic", "physical", "interactive" ], "concepts": [ "mlsys.human-oversight", "mlsys.contestability", "mlsys.automation-bias", "mlsys.authority-handover", "mlsys.human-ai-interaction" ], "related_concepts": [ "mlsys.approval-gate", "mlsys.selective-prediction", "mlsys.irreversible-effects" ], "taught_in": [ { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" }, { "volume": "vol4", "chapter": "Supervisory Intervention", "url": "https://mlsysbook.ai/vol4/14_intervention/14_intervention.html" }, { "volume": "vol4", "chapter": "Responsible Physical AI", "url": "https://mlsysbook.ai/vol4/17_responsibility/17_responsibility.html" } ] }, { "code": "Q5.4", "id": "governance-regulation", "label": "AI governance, audit & regulation", "definition": "Organizational and legal mechanisms that identify, assess, and control AI risk across a system's life, including risk-management frameworks, impact assessments, audits, and regulation.", "includes": [ "NIST AI RMF and ISO/IEC 42001 management systems; internal and external audits.", "Risk-tiered regulation (EU AI Act) and frontier-model capability thresholds." ], "excludes": [ "Data-protection mechanisms such as differential privacy ([[security-privacy]])", "Safety-standard certification ([[safety-cases]])" ], "contexts": [ "classical-ml", "deep-learning", "generative", "agentic" ], "concepts": [ "mlsys.ai-risk-management", "mlsys.impact-assessment", "mlsys.algorithmic-audit", "mlsys.risk-tiered-regulation", "mlsys.frontier-safety-framework", "mlsys.decision-accountability", "mlsys.content-provenance" ], "related_concepts": [ "mlsys.safety-case", "mlsys.privacy", "mlsys.incident-response" ], "taught_in": [ { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol2", "chapter": "Responsible AI", "url": "https://mlsysbook.ai/vol2/16_responsible_ai/16_responsible_ai.html" }, { "volume": "vol4", "chapter": "Deployment Release", "url": "https://mlsysbook.ai/vol4/16_release/16_release.html" }, { "volume": "vol4", "chapter": "Responsible Physical AI", "url": "https://mlsysbook.ai/vol4/17_responsibility/17_responsibility.html" } ] } ] }, { "code": "Q6", "id": "sustainability-cost", "label": "Sustainability & cost", "question": "What does the system cost in energy, carbon, water, and money over its lifetime?", "scope": "Accounting for environmental and monetary consequences of ML systems under declared boundaries, and the decisions that reduce them per useful outcome.", "subjects": [ { "code": "Q6.1", "id": "energy-carbon", "label": "Energy & carbon accounting", "definition": "Measuring the energy a workload consumes and converting it to operational emissions under declared boundaries, and scheduling work to reduce both.", "includes": [ "Energy measurement and energy efficiency (work per joule)", "Grid carbon intensity and operational carbon", "Carbon-aware placement and scheduling" ], "excludes": [ "Facility power, cooling, and PUE ([[datacenter-facilities]])", "Manufacturing emissions and hardware end of life ([[embodied-lifecycle]])" ], "contexts": [ "distributed", "generative", "batch" ], "concepts": [ "mlsys.energy", "mlsys.carbon", "mlsys.energy-efficiency", "mlsys.carbon-intensity", "mlsys.operational-carbon", "mlsys.emission-scopes", "mlsys.carbon-aware-scheduling", "mlsys.energy-per-inference", "mlsys.energy-measurement", "mlsys.idle-dynamic-power", "mlsys.demand-response", "mlsys.rebound-effect" ], "related_concepts": [ "mlsys.pue", "mlsys.power-delivery" ], "taught_in": [ { "volume": "vol1", "chapter": "Benchmarking", "url": "https://mlsysbook.ai/vol1/12_benchmarking/12_benchmarking.html" }, { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol2", "chapter": "Sustainable AI", "url": "https://mlsysbook.ai/vol2/15_sustainable_ai/15_sustainable_ai.html" } ] }, { "code": "Q6.2", "id": "embodied-lifecycle", "label": "Embodied carbon & lifecycle impact", "definition": "Environmental impacts beyond operating energy, from manufacturing emissions and water to hardware lifetime and disposal, assessed over the full hardware lifecycle.", "includes": [ "Embodied carbon and lifecycle assessment", "Water consumption of training and serving", "Hardware lifetime, refresh, and e-waste" ], "excludes": [ "Operational emissions from electricity ([[energy-carbon]])", "Model retirement and decommissioning ([[retirement]])" ], "contexts": [ "edge", "distributed" ], "concepts": [ "mlsys.embodied-carbon", "mlsys.lifecycle-assessment", "mlsys.water-footprint", "mlsys.hardware-lifetime", "mlsys.e-waste" ], "related_concepts": [], "taught_in": [ { "volume": "vol2", "chapter": "Sustainable AI", "url": "https://mlsysbook.ai/vol2/15_sustainable_ai/15_sustainable_ai.html" } ] }, { "code": "Q6.3", "id": "cost-economics", "label": "Cost, TCO & unit economics", "definition": "Monetary accounting for ML systems, from total cost of ownership to cost per token or accepted outcome, and the architectural choices that lower cost at fixed quality.", "includes": [ "Total cost of ownership and cost attribution (FinOps)", "Cost per token and cost per accepted outcome", "Model routing and cascades chosen for cost" ], "excludes": [ "Capacity provisioning and quotas ([[capacity-provisioning]])", "Spot and preemption scheduling mechanics ([[cluster-scheduling]])" ], "contexts": [ "generative", "agentic", "interactive" ], "concepts": [ "mlsys.unit-cost", "mlsys.tco", "mlsys.cost-per-token", "mlsys.cost-attribution", "mlsys.data-transfer-cost" ], "related_concepts": [ "mlsys.capacity-planning", "mlsys.prefix-cache", "mlsys.model-routing" ], "taught_in": [ { "volume": "vol1", "chapter": "Model Serving", "url": "https://mlsysbook.ai/vol1/13_model_serving/13_model_serving.html" }, { "volume": "vol1", "chapter": "Responsible Engineering", "url": "https://mlsysbook.ai/vol1/15_responsible_engr/15_responsible_engr.html" }, { "volume": "vol2", "chapter": "Fleet Orchestration", "url": "https://mlsysbook.ai/vol2/08_fleet_orchestration/08_fleet_orchestration.html" }, { "volume": "vol2", "chapter": "Fleet Efficiency", "url": "https://mlsysbook.ai/vol2/09_performance_engineering/09_performance_engineering.html" }, { "volume": "vol2", "chapter": "ML Operations at Scale", "url": "https://mlsysbook.ai/vol2/12_ops_scale/12_ops_scale.html" }, { "volume": "vol3", "chapter": "Agent Economics", "url": "https://mlsysbook.ai/vol3/19_agent_economics/19_agent_economics.html" } ] } ] } ] }, { "code": "T", "id": "system-types", "label": "System types", "question": "What is distinctive when the system acts as an agent or in the physical world?", "areas": [ { "code": "T1", "id": "agentic", "label": "Agentic systems", "question": "What does it take to engineer models that plan and act through tools over many steps?", "scope": "Context engineering and agent memory, tool and action interfaces, the agent harness and its control loop, durable agent state, and multi-agent coordination.", "subjects": [ { "code": "T1.1", "id": "context-memory", "label": "Context engineering & agent memory", "definition": "Deciding what information enters each model call over a long task, and maintaining persistent memory the agent can write, consolidate, and retrieve across calls and sessions.", "includes": [ "Working-set selection, ordering for prefix reuse, compaction, and long-context degradation.", "Long-term memory write paths, consolidation, expiry, and invalidation." ], "excludes": [ "Retrieval and vector indexes in general ([[retrieval-rag]])", "KV-cache management ([[llm-inference]])" ], "contexts": [ "agentic", "generative", "interactive" ], "concepts": [ "mlsys.context-compaction", "mlsys.agent-memory", "mlsys.context-selection", "mlsys.context-staleness" ], "related_concepts": [ "mlsys.retrieval", "mlsys.prefix-cache", "mlsys.kv-cache", "mlsys.prompt-injection", "mlsys.context", "mlsys.context-degradation" ], "taught_in": [ { "volume": "vol3", "chapter": "Context Engineering", "url": "https://mlsysbook.ai/vol3/04_context_engineering/04_context_engineering.html" }, { "volume": "vol3", "chapter": "Long-Term Memory", "url": "https://mlsysbook.ai/vol3/06_long_term_memory/06_long_term_memory.html" } ] }, { "code": "T1.2", "id": "tool-interfaces", "label": "Tool use & action interfaces", "definition": "The interfaces through which a model acts on the world, including structured tool calls, tool discovery protocols, computer use, and the effects and authority those actions carry.", "includes": [ "Tool schemas, preconditions, function calling, and MCP-style discovery.", "Computer-use agents and the handling of irreversible effects." ], "excludes": [ "Sandboxing and capability isolation ([[access-isolation]])", "Prompt injection ([[ml-attacks]])" ], "contexts": [ "agentic", "generative" ], "concepts": [ "mlsys.tool-contract", "mlsys.irreversible-effects", "mlsys.function-calling", "mlsys.model-context-protocol", "mlsys.computer-use", "mlsys.agent-credentials" ], "related_concepts": [ "mlsys.authorization", "mlsys.isolation", "mlsys.idempotency", "mlsys.prompt-injection", "mlsys.grammar" ], "taught_in": [ { "volume": "vol3", "chapter": "The Foundation Model", "url": "https://mlsysbook.ai/vol3/02_foundation_model/02_foundation_model.html" }, { "volume": "vol3", "chapter": "Tool Calling", "url": "https://mlsysbook.ai/vol3/07_tool_calling/07_tool_calling.html" } ] }, { "code": "T1.3", "id": "agent-harness", "label": "Agent harness & control loop", "definition": "The runtime that owns an agent's loop of model calls and tool actions, enforcing budgets, stopping rules, and approval gates and recording the trajectory.", "includes": [ "Reason-act loops, workflows versus model-directed loops, and plan revision.", "Turn, token, time, and spend budgets; stopping rules; human approval gates." ], "excludes": [ "Inference-time search and reasoning budgets per call ([[test-time-compute]])", "Trajectory-level evaluation ([[generative-agent-eval]])" ], "contexts": [ "agentic", "generative", "interactive" ], "concepts": [ "mlsys.trajectory", "mlsys.planning", "mlsys.agent-harness", "mlsys.agent-budget", "mlsys.approval-gate", "mlsys.agent-interruption", "mlsys.agent-loop-detection", "mlsys.agentic-workload" ], "related_concepts": [ "mlsys.agent-evaluation", "mlsys.human-oversight", "mlsys.unit-cost", "mlsys.decision-policy" ], "taught_in": [ { "volume": "vol3", "chapter": "Introduction", "url": "https://mlsysbook.ai/vol3/01_introduction/01_introduction.html" }, { "volume": "vol3", "chapter": "The Agent Harness", "url": "https://mlsysbook.ai/vol3/10_agent_harness/10_agent_harness.html" } ] }, { "code": "T1.4", "id": "durable-execution", "label": "Durable execution & agent state", "definition": "Persisting an agent's progress so long-running trajectories survive crashes, pauses, and redeployments without repeating completed effects, and can be replayed for debugging.", "includes": [ "Event-sourced trajectory logs, resume protocols, and record-and-replay.", "Pausing for human or external events and resuming safely." ], "excludes": [ "General checkpointing, idempotency, and sagas ([[reliability]])" ], "contexts": [ "agentic", "distributed" ], "concepts": [ "mlsys.durable-execution", "mlsys.deterministic-replay", "mlsys.suspend-resume" ], "related_concepts": [ "mlsys.checkpoint", "mlsys.idempotency", "mlsys.compensation", "mlsys.fencing", "mlsys.workflow-orchestration" ], "taught_in": [ { "volume": "vol3", "chapter": "Durable Execution", "url": "https://mlsysbook.ai/vol3/11_durable_execution/11_durable_execution.html" }, { "volume": "vol3", "chapter": "Failure Recovery", "url": "https://mlsysbook.ai/vol3/12_failure_recovery/12_failure_recovery.html" } ] }, { "code": "T1.5", "id": "multi-agent", "label": "Multi-agent orchestration", "definition": "Decomposing a task across several model-driven agents and coordinating their delegation, shared state, and results, including when coordination costs outweigh the benefit.", "includes": [ "Orchestrator-worker and peer topologies, typed task envelopes, and delegation.", "Coordination failures and correlated errors among agents sharing a base model." ], "excludes": [ "Distributed consensus for infrastructure state ([[coordination]])" ], "contexts": [ "agentic", "generative", "distributed" ], "concepts": [ "mlsys.multi-agent-orchestration", "mlsys.shared-agent-state", "mlsys.coordination-failure", "mlsys.correlated-agent-errors" ], "related_concepts": [ "mlsys.baseline", "mlsys.agent-evaluation", "mlsys.fencing" ], "taught_in": [ { "volume": "vol3", "chapter": "Multi-Agent Coordination", "url": "https://mlsysbook.ai/vol3/18_multi_agent/18_multi_agent.html" } ] } ] }, { "code": "T2", "id": "embodied", "label": "Embodied & physical systems", "question": "What changes when ML closes the loop with sensors, actuators, and the physical world?", "scope": "Sensing and perception, state estimation and mapping, motion planning and control, real-time and mixed-criticality computing, and robot learning from demonstration and simulation.", "subjects": [ { "code": "T2.1", "id": "perception-sensing", "label": "Sensing, perception & fusion", "definition": "Turning time-stamped measurements from physical sensors into task-relevant estimates of the scene, including calibration, synchronization, and fusion across modalities.", "includes": [ "Camera, LiDAR, radar, event, and tactile sensing; calibration chains.", "Detection, segmentation, learned depth, and multi-sensor fusion." ], "excludes": [ "Sensor spoofing ([[ml-attacks]])" ], "contexts": [ "physical", "edge", "streaming" ], "concepts": [ "mlsys.sensing", "mlsys.robot-perception", "mlsys.sensor-fusion", "mlsys.time-synchronization", "mlsys.perception-latency-budget", "mlsys.sensor-interference" ], "related_concepts": [ "mlsys.observation-age", "mlsys.distribution-shift", "mlsys.streaming-asr", "mlsys.voice-pipeline" ], "taught_in": [ { "volume": "vol4", "chapter": "Sensor Perception", "url": "https://mlsysbook.ai/vol4/08_perception/08_perception.html" } ] }, { "code": "T2.2", "id": "state-estimation", "label": "State estimation, localization & mapping", "definition": "Inferring a robot's and its environment's latent state and uncertainty over time, through recursive filtering, localization and mapping, and learned predictive world models.", "includes": [ "Kalman and particle filters, tracking and data association, SLAM, metric and semantic maps.", "Learned world models used for prediction and planning." ], "excludes": [ "Software observability of services ([[monitoring]])" ], "contexts": [ "physical", "edge", "streaming" ], "concepts": [ "mlsys.state-estimation", "mlsys.state-observability", "mlsys.recursive-filtering", "mlsys.slam", "mlsys.world-model", "mlsys.multi-object-tracking" ], "related_concepts": [ "mlsys.sensing", "mlsys.calibration" ], "taught_in": [ { "volume": "vol4", "chapter": "Spatial Memory", "url": "https://mlsysbook.ai/vol4/09_memory/09_memory.html" } ] }, { "code": "T2.3", "id": "planning-control", "label": "Motion planning & control", "definition": "Generating feasible, collision-free motions and closing feedback loops that drive a physical system along them under dynamics, contact, and actuation limits.", "includes": [ "Sampling- and optimization-based motion planning, task and motion planning.", "Feedback control, MPC, and contact-rich manipulation." ], "excludes": [ "Runtime safety filters over a planner's output ([[runtime-enforcement]])" ], "contexts": [ "physical", "edge" ], "concepts": [ "mlsys.dynamics", "mlsys.feedback-control", "mlsys.trajectory-feasibility", "mlsys.contact-compliance", "mlsys.motion-planning", "mlsys.model-predictive-control", "mlsys.actuator-limits", "mlsys.task-and-motion-planning" ], "related_concepts": [ "mlsys.stopping-envelope", "mlsys.planning" ], "taught_in": [ { "volume": "vol4", "chapter": "Grounded Intent", "url": "https://mlsysbook.ai/vol4/10_intent/10_intent.html" }, { "volume": "vol4", "chapter": "Trajectory Planning", "url": "https://mlsysbook.ai/vol4/11_planning/11_planning.html" } ] }, { "code": "T2.4", "id": "real-time-systems", "label": "Real-time & mixed-criticality computing", "definition": "Guaranteeing that sensing, inference, and control complete within deadlines on shared embedded hardware, and isolating safety-critical functions from less trusted ones.", "includes": [ "Multi-rate scheduling, response-time analysis, WCET, and real-time buses.", "Mixed-criticality partitioning and hypervisors on shared silicon." ], "excludes": [ "Throughput-oriented profiling and utilization ([[profiling-utilization]])" ], "contexts": [ "physical", "edge", "streaming" ], "concepts": [ "mlsys.observation-age", "mlsys.feedback-delay", "mlsys.wcet", "mlsys.real-time-scheduling", "mlsys.mixed-criticality", "mlsys.watchdog-timer", "mlsys.robotics-middleware", "mlsys.onboard-compute-partitioning", "mlsys.interrupt-latency", "mlsys.priority-inversion" ], "related_concepts": [ "mlsys.execution-time", "mlsys.placement", "mlsys.thermal-dynamics", "mlsys.isolation" ], "taught_in": [ { "volume": "vol4", "chapter": "The Nervous System", "url": "https://mlsysbook.ai/vol4/04_nervous/04_nervous.html" }, { "volume": "vol4", "chapter": "Workload Placement", "url": "https://mlsysbook.ai/vol4/13_placement/13_placement.html" } ] }, { "code": "T2.5", "id": "robot-learning", "label": "Robot learning & sim-to-real", "definition": "Learning robot policies from teleoperated demonstrations, simulation, and pretrained vision-language backbones, and transferring them to physical hardware.", "includes": [ "Vision-language-action models, action chunking, and cross-embodiment pretraining.", "Teleoperation data collection, domain randomization, and sim-to-real transfer." ], "excludes": [ "Imitation and reinforcement learning as general paradigms ([[learning-paradigms]])", "Closed-loop robot evaluation statistics ([[evaluation]])" ], "contexts": [ "physical", "deep-learning", "edge" ], "concepts": [ "mlsys.sim-to-real", "mlsys.vla-model", "mlsys.teleoperation-data", "mlsys.action-chunking", "mlsys.domain-randomization", "mlsys.diffusion-policy", "mlsys.parallel-simulation" ], "related_concepts": [ "mlsys.imitation-learning", "mlsys.reinforcement-learning", "mlsys.distribution-shift" ], "taught_in": [ { "volume": "vol4", "chapter": "The Cognitive Brain", "url": "https://mlsysbook.ai/vol4/03_brain/03_brain.html" }, { "volume": "vol4", "chapter": "Physical Data", "url": "https://mlsysbook.ai/vol4/05_data/05_data.html" }, { "volume": "vol4", "chapter": "Policy Training", "url": "https://mlsysbook.ai/vol4/06_training/06_training.html" } ] } ] } ] } ], "concepts": [ { "id": "mlsys.requirements", "label": "System requirements", "aliases": [], "definition": "Measurable desired behavior and constraints for a specified workload.", "background": false, "home": "F1.1", "home_id": "requirements-slos" }, { "id": "mlsys.baseline", "label": "Baseline", "aliases": [], "definition": "A reference method or system used to justify the value of a proposed change.", "background": false, "home": "F1.1", "home_id": "requirements-slos" }, { "id": "mlsys.proxy-objective", "label": "Proxy objective", "aliases": [], "definition": "A measurable target used in place of the outcome actually desired.", "background": false, "home": "F1.3", "home_id": "objectives-decisions" 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"data-quality" }, { "id": "mlsys.labels", "label": "Label quality", "aliases": [], "definition": "Correctness, consistency, and availability of supervisory targets.", "background": false, "home": "P1.1", "home_id": "data-collection" }, { "id": "mlsys.features", "label": "Feature engineering", "aliases": [], "definition": "Constructing model inputs whose semantics and availability match the task.", "background": false, "home": "P1.3", "home_id": "data-quality" }, { "id": "mlsys.missing-data", "label": "Missing data", "aliases": [], "definition": "Absent observations whose pattern and treatment can change learned behavior.", "background": false, "home": "P1.3", "home_id": "data-quality" }, { "id": "mlsys.leakage", "label": "Data leakage", "aliases": [], "definition": "Information unavailable at the intended prediction boundary entering development or evaluation.", "background": false, "home": "P1.6", "home_id": "data-leakage-skew" }, { "id": "mlsys.point-in-time", "label": "Point-in-time 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"home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.batch-inference", "label": "Batch inference", "aliases": [ "offline prediction" ], "definition": "Computing predictions for many inputs in scheduled jobs and storing them for later lookup.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.model-server", "label": "Model server", "aliases": [ "inference server" ], "definition": "A service that loads model artifacts and executes inference requests behind a network interface, managing versions, batching, and resources.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.multi-model-serving", "label": "Multi-model serving", "aliases": [], "definition": "Hosting many models or adapters on shared serving resources, trading isolation and load time against utilization.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.model-cascade", "label": "Model cascade", "aliases": [], "definition": "Sending a request to cheaper models first and escalating to more capable models only when a confidence or quality criterion is not met.", "background": false, "home": "P4.2", "home_id": "compound-ai" }, { "id": "mlsys.model-routing", "label": "Model routing", "aliases": [], "definition": "Choosing which model serves each request from request features, predicted quality, and cost.", "background": false, "home": "P4.2", "home_id": "compound-ai" }, { "id": "mlsys.ttft", "label": "Time to first token", "aliases": [ "TTFT" ], "definition": "Latency from request arrival until the first generated token is returned, dominated by queueing and prefill.", "background": false, "home": "P4.3", "home_id": "latency-goodput" }, { "id": "mlsys.tpot", "label": "Time per output token", "aliases": [ "TPOT", "inter-token latency" ], "definition": "Average latency between successive generated tokens after the first, dominated by decode step time.", "background": false, "home": 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"definition": "Delay before a newly started replica can serve, from provisioning, loading weights, and warming runtime state.", "background": false, "home": "P4.4", "home_id": "request-scheduling" }, { "id": "mlsys.chunked-prefill", "label": "Chunked prefill", "aliases": [], "definition": "Splitting a long prompt's prefill into chunks interleaved with decode steps to bound per-iteration latency.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": "mlsys.pd-disaggregation", "label": "Prefill-decode disaggregation", "aliases": [ "disaggregated serving" ], "definition": "Running prefill and decode on separate resource pools, transferring KV state between them, so each phase is provisioned and batched independently.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": "mlsys.sampling-controls", "label": "Decoding and sampling controls", "aliases": [], "definition": "Parameters and rules, such as greedy, temperature, top-k, and nucleus sampling, that determine how tokens are chosen from a model's distribution.", "background": false, "home": "P4.6", "home_id": "structured-generation" }, { "id": "mlsys.constrained-decoding", "label": "Constrained decoding", "aliases": [ "grammar-guided decoding" ], "definition": "Masking disallowed tokens at each step, typically from a grammar or schema automaton, so output satisfies a structural constraint.", "background": false, "home": "P4.6", "home_id": "structured-generation" }, { "id": "mlsys.output-validation", "label": "Output validation", "aliases": [], "definition": "Checking generated output against a schema or rules after generation and repairing, retrying, or rejecting it on failure.", "background": false, "home": "P4.6", "home_id": "structured-generation" }, { "id": "mlsys.reasoning-budget", "label": "Reasoning budget", "aliases": [ "thinking budget" ], "definition": "A limit on the intermediate tokens or steps a model may spend before answering, set to trade quality 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"aliases": [ "PRM" ], "definition": "A verifier that scores intermediate reasoning steps rather than only final answers.", "background": false, "home": "P4.7", "home_id": "test-time-compute" }, { "id": "mlsys.inference-time-search", "label": "Inference-time search", "aliases": [], "definition": "Exploring a tree of partial solutions, as in beam or tree search, guided by a scorer, to find a higher-quality output.", "background": false, "home": "P4.7", "home_id": "test-time-compute" }, { "id": "mlsys.test-time-scaling", "label": "Test-time scaling", "aliases": [], "definition": "The empirical relation between inference compute spent per query and output quality, used to allocate compute between model size and inference.", "background": false, "home": "P4.7", "home_id": "test-time-compute" }, { "id": "mlsys.rag", "label": "Retrieval-augmented generation", "aliases": [ "RAG" ], "definition": "Conditioning generation on documents retrieved for the request so outputs can draw on information outside the model's parameters.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.dense-retrieval", "label": "Dense retrieval", "aliases": [], "definition": "Retrieving records by similarity between learned embeddings of query and records.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.lexical-retrieval", "label": "Lexical retrieval", "aliases": [ "sparse retrieval", "BM25" ], "definition": "Retrieving records by weighted term overlap with the query using an inverted index, as in BM25.", "background": true, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.reranking", "label": "Reranking", "aliases": [], "definition": "Reordering a retrieved candidate set with a more expensive relevance model before use.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.model-conversion", "label": "Model conversion", "aliases": [ "model export" ], "definition": "Translating a trained model into the format and operator set of a target runtime, possibly changing numerics and supported operations.", "background": false, "home": "P4.9", "home_id": "on-device-deployment" }, { "id": "mlsys.on-device-runtime", "label": "On-device inference runtime", "aliases": [], "definition": "An interpreter or compiled engine that executes models on mobile, embedded, or microcontroller targets within their memory and operator limits.", "background": false, "home": "P4.9", "home_id": "on-device-deployment" }, { "id": "mlsys.ota-model-update", "label": "Over-the-air model update", "aliases": [ "OTA update" ], "definition": "Delivering new model versions to deployed devices remotely, with staging, verification, and rollback across the device fleet.", "background": false, "home": "P4.9", "home_id": "on-device-deployment" }, { "id": "mlsys.device-cloud-partitioning", "label": "Device-cloud partitioning", "aliases": [ "split computing", "offloading" ], "definition": "Splitting inference between device and server, by layer or by model, to balance latency, energy, bandwidth, and privacy.", "background": false, "home": "P4.9", "home_id": "on-device-deployment" }, { "id": "mlsys.device-heterogeneity", "label": "Device heterogeneity", "aliases": [], "definition": "Variation in hardware, operating system, and runtime capability across a deployed device fleet that a single model release must accommodate.", "background": false, "home": "P4.9", "home_id": "on-device-deployment" }, { "id": "mlsys.model-registry", "label": "Model registry", "aliases": [], "definition": "A versioned store of model artifacts with metadata, lineage, and lifecycle stage used to govern promotion and deployment.", "background": false, "home": "P5.1", "home_id": "release-management" }, { "id": "mlsys.shadow-deployment", "label": "Shadow deployment", "aliases": [ "shadow mode" ], "definition": "Running a candidate model on live traffic without serving its outputs, to compare it against the production model.", "background": false, "home": "P5.1", "home_id": "release-management" }, { "id": "mlsys.ml-ci-cd", "label": "CI/CD for ML", "aliases": [], "definition": "Automated building, testing, and deployment of ML code, data checks, and model artifacts on each change.", "background": false, "home": "P5.1", "home_id": "release-management" }, { "id": "mlsys.release-gate", "label": "Release gate", "aliases": [ "validation gate" ], "definition": "A predeclared set of checks a candidate model must pass before it advances to a wider deployment stage.", "background": false, "home": "P5.1", "home_id": "release-management" }, { "id": "mlsys.drift-detection", "label": "Drift detection", "aliases": [], "definition": "Statistical tests or distances comparing production and reference distributions of inputs, predictions, or outcomes to flag change.", "background": false, "home": "P5.2", "home_id": "monitoring" }, { "id": "mlsys.inference-logging", "label": "Prediction logging", "aliases": [], "definition": "Recording requests, model versions, outputs, and later outcomes so production behavior can be analyzed, debugged, and reused as data.", "background": false, "home": "P5.2", "home_id": "monitoring" }, { "id": "mlsys.alerting", "label": "Alerting", "aliases": [], "definition": "Rules that turn monitored signals into notifications for responders, tuned to balance missed incidents against alert fatigue.", "background": false, "home": "P5.2", "home_id": "monitoring" }, { "id": "mlsys.concept-drift", "label": "Concept drift", "aliases": [], "definition": "Change over time in the relationship between inputs and targets, so the same input warrants a different prediction.", "background": false, "home": "P5.3", "home_id": "drift-retraining" }, { "id": "mlsys.continuous-training", "label": "Continuous training", "aliases": [ "CT" ], "definition": "An automated pipeline that retrains, validates, and proposes models on a schedule or when triggered by data or performance signals.", "background": false, "home": "P5.3", "home_id": "drift-retraining" }, { "id": "mlsys.postmortem", "label": "Blameless postmortem", "aliases": [], "definition": "A written analysis after an incident of its timeline, causes, and corrective actions, focused on system fixes rather than blame.", "background": true, "home": "P5.4", "home_id": "incident-response" }, { "id": "mlsys.failure-attribution", "label": "ML failure attribution", "aliases": [], "definition": "Determining whether a production failure stems from data, model, code, configuration, or infrastructure, or from an adversary.", "background": false, "home": "P5.4", "home_id": "incident-response" }, { "id": "mlsys.configuration-management", "label": "ML configuration management", "aliases": [], "definition": "Treating model, feature, and pipeline configuration as versioned, reviewed, and tested code.", "background": false, "home": "P5.5", "home_id": "pipelines-reproducibility" }, { "id": "mlsys.infrastructure-as-code", "label": "Infrastructure as code", "aliases": [ "IaC" ], "definition": "Declaring compute, storage, and service infrastructure in versioned machine-readable files from which environments are provisioned.", "background": true, "home": "P5.5", "home_id": "pipelines-reproducibility" }, { "id": "mlsys.ml-platform", "label": "ML platform", "aliases": [], "definition": "Shared internal infrastructure and services that standardize how teams train, deploy, and operate models.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.technical-debt", "label": "ML technical debt", "aliases": [], "definition": "Accumulated system costs from shortcuts in ML code, data dependencies, configuration, and pipelines that make future change slower and riskier.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.entanglement", "label": "Entanglement", "aliases": [ "CACE" ], "definition": "The property that changing any input, feature, or hyperparameter of a model can change its behavior on everything else.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.undeclared-consumers", "label": "Undeclared consumers", "aliases": [], "definition": "Systems that use a model's outputs without the model owner's knowledge, creating hidden coupling.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.mlops-maturity", "label": "MLOps maturity", "aliases": [], "definition": "Staged levels of automation, from manual training and deployment to automated continuous training and delivery.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.ml-roles", "label": "ML team roles", "aliases": [], "definition": "The division of responsibilities among data, modeling, platform, and operations roles that owns an ML system across its lifecycle.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.model-deprecation", "label": "Model deprecation", "aliases": [], "definition": "Planned withdrawal of a model from service, including notifying and migrating its consumers before removing it.", "background": false, "home": "P5.7", "home_id": "retirement" }, { "id": "mlsys.artifact-archival", "label": "Artifact archival", "aliases": [], "definition": "Preserving retired models, data references, and evaluation records needed for audit, reproduction, or rollback after service ends.", "background": false, "home": "P5.7", "home_id": "retirement" }, { "id": "mlsys.data-retention", "label": "Data retention", "aliases": [], "definition": "Policy and mechanisms that set how long data, logs, and derived artifacts are kept and when they are deleted.", "background": false, "home": "P5.7", "home_id": "retirement" }, { "id": "mlsys.model-flops-utilization", "label": "Model FLOPs utilization", "aliases": [ "MFU" ], "definition": "Model-required floating-point operations per second achieved, divided by the aggregate peak FLOP rate of the hardware used, excluding recomputation.", "background": false, "home": "Q1.1", "home_id": "profiling-utilization" }, { "id": "mlsys.bottleneck-analysis", "label": "Bottleneck analysis", "aliases": [], "definition": "Identifying the resource or stage, such as compute, memory bandwidth, I/O, or communication, whose capacity limits end-to-end throughput or latency for a workload.", "background": false, "home": "Q1.1", "home_id": "profiling-utilization" }, { "id": "mlsys.training-goodput", "label": "Training goodput", "aliases": [ "ML goodput" ], "definition": "The fraction of allocated accelerator time that produces retained training progress, after losses to scheduling, failures, restarts, and lost work.", "background": false, "home": "Q1.1", "home_id": "profiling-utilization" }, { "id": "mlsys.pruning", "label": "Pruning", "aliases": [], "definition": "Removing weights, neurons, channels, or other structures judged unimportant from a network, usually followed by fine-tuning to recover quality.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.structured-pruning", "label": "Structured pruning", "aliases": [], "definition": "Pruning whole channels, filters, heads, or layers so that the remaining model stays dense and runs faster without sparse-execution support.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.low-rank-factorization", "label": "Low-rank factorization", "aliases": [ "low-rank approximation" ], "definition": "Approximating a weight matrix or tensor by a product of smaller factors of reduced rank to cut parameters and operations.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.quantization-aware-training", "label": "Quantization-aware training", "aliases": [ "QAT" ], "definition": "Training or fine-tuning with simulated quantization in the forward pass so the model adapts to reduced precision, in contrast to post-training quantization.", 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reducing operations and parameters.", "background": false, "home": "F2.2", "home_id": "architecture-blocks" }, { "id": "mlsys.efficiency-frontier", "label": "Accuracy-efficiency frontier", "aliases": [ "Pareto frontier" ], "definition": "The set of model configurations for which no alternative improves quality without worsening a stated cost measure on a stated platform.", "background": false, "home": "Q1.4", "home_id": "hardware-aware-design" }, { "id": "mlsys.model-hardware-co-design", "label": "Model-hardware co-design", "aliases": [], "definition": "Jointly choosing model architecture and hardware or kernel design so that each exploits the other's structure.", "background": false, "home": "Q1.4", "home_id": "hardware-aware-design" }, { "id": "mlsys.retry-backoff", "label": "Retry with backoff", "aliases": [], "definition": "Reissuing a failed request after increasing, typically jittered, delays and within a retry budget, so that transient faults recover without overloading dependencies.", "background": true, "home": "Q2.1", "home_id": "failure-semantics" }, { "id": "mlsys.circuit-breaker", "label": "Circuit breaker", "aliases": [], "definition": "A client-side guard that stops sending requests to a failing dependency after an error threshold and probes it before resuming traffic.", "background": true, "home": "Q2.1", "home_id": "failure-semantics" }, { "id": "mlsys.replication-failover", "label": "Replication and failover", "aliases": [], "definition": "Maintaining redundant replicas of a service or state and redirecting work to a healthy replica when one fails.", "background": false, "home": "Q2.1", "home_id": "failure-semantics" }, { "id": "mlsys.graceful-degradation", "label": "Graceful degradation", "aliases": [], "definition": "Continuing to serve with reduced quality, features, or freshness under failure or overload instead of failing outright.", "background": false, "home": "Q2.1", "home_id": "failure-semantics" }, { "id": "mlsys.checkpoint-interval", "label": "Optimal checkpoint interval", "aliases": [ "Young-Daly interval" ], "definition": "The checkpoint period that minimizes expected lost time, balancing checkpoint write cost against expected rework, approximately the square root of twice the write cost times MTBF.", "background": false, "home": "Q2.2", "home_id": "checkpoint-recovery" }, { "id": "mlsys.async-checkpointing", "label": "Asynchronous checkpointing", "aliases": [], "definition": "Snapshotting training state quickly to host memory and persisting it in the background so computation resumes before the durable write completes.", "background": false, "home": "Q2.2", "home_id": "checkpoint-recovery" }, { "id": "mlsys.silent-data-corruption", "label": "Silent data corruption", "aliases": [ "SDC" ], "definition": "Hardware producing wrong computational results without raising an error or crash, so the corruption propagates undetected.", "background": false, "home": "Q2.3", "home_id": "hardware-faults" }, { "id": "mlsys.mtbf", "label": "Mean time between failures", "aliases": [ "MTBF" ], "definition": "The expected operating time between failures of a component or system; for independent components, system MTBF shrinks roughly in inverse proportion to component count.", "background": false, "home": "Q2.3", "home_id": "hardware-faults" }, { "id": "mlsys.availability", "label": "Availability", "aliases": [], "definition": "The fraction of time or requests for which a system performs its specified function, approximately MTBF divided by MTBF plus mean time to repair.", "background": false, "home": "Q2.3", "home_id": "hardware-faults" }, { "id": "mlsys.gray-failure", "label": "Gray failure", "aliases": [], "definition": "A partial or degraded failure that some observers detect and others do not, such as a slow or lossy node that still passes health checks.", "background": false, "home": "Q2.3", "home_id": "hardware-faults" }, { "id": "mlsys.failure-domain", "label": "Failure domain", "aliases": [ "correlated failure" ], "definition": "A set of components that can fail together from a shared cause, such as a rack, power feed, switch, or software release.", "background": false, "home": "Q2.3", "home_id": "hardware-faults" }, { "id": "mlsys.abft", "label": "Algorithm-based fault tolerance", "aliases": [ "ABFT" ], "definition": "Detecting or correcting computational errors by carrying checksums through an algorithm, such as matrix multiplication, and verifying invariants on its outputs.", "background": false, "home": "Q2.3", "home_id": "hardware-faults" }, { "id": "mlsys.chaos-engineering", "label": "Chaos engineering", "aliases": [], "definition": "Running controlled experiments that inject real-world failures into production-like systems to test a hypothesis about steady-state behavior.", "background": true, "home": "Q2.4", "home_id": "fault-injection" }, { "id": "mlsys.game-day", "label": "Game day", "aliases": [ "disaster recovery exercise" ], "definition": "A scheduled exercise in which teams deliberately trigger failure scenarios to rehearse detection, response, and recovery procedures.", "background": true, "home": "Q2.4", "home_id": "fault-injection" }, { "id": "mlsys.hardware-fault-injection", "label": "Hardware fault injection", "aliases": [], "definition": "Emulating hardware faults such as bit flips in weights, activations, or registers to measure how errors propagate to model outputs.", "background": false, "home": "Q2.4", "home_id": "fault-injection" }, { "id": "mlsys.blast-radius", "label": "Blast radius", "aliases": [], "definition": "The extent of users, components, or effects that a single fault, change, or experiment can impact.", "background": false, "home": "Q2.4", "home_id": "fault-injection" }, { "id": "mlsys.backdoor", "label": "Backdoor attack", "aliases": [ "trojan attack" ], "definition": "Implanting a hidden behavior in a model, via poisoned data or modified weights, that activates only on inputs containing an attacker-chosen trigger.", "background": false, "home": "Q3.1", "home_id": "ml-attacks" }, { "id": "mlsys.jailbreak", "label": "Jailbreak", "aliases": [], "definition": "A user-crafted input that induces a model to bypass its safety training or usage policy and produce disallowed output.", "background": false, "home": "Q3.1", "home_id": "ml-attacks" }, { "id": "mlsys.adversarial-training", "label": "Adversarial training", "aliases": [], "definition": "Training on adversarially perturbed inputs generated during training to increase robustness within a specified perturbation set.", "background": false, "home": "Q3.1", "home_id": "ml-attacks" }, { "id": "mlsys.model-extraction", "label": "Model extraction", "aliases": [ "model stealing" ], "definition": "Reconstructing a functionally equivalent copy or the parameters of a model by querying its prediction interface.", "background": false, "home": "Q3.2", "home_id": "model-supply-chain" }, { "id": "mlsys.model-watermarking", 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errors.", "background": false, "home": "Q5.3", "home_id": "human-oversight" }, { "id": "mlsys.authority-handover", "label": "Authority handover", "aliases": [ "takeover", "shared autonomy" ], "definition": "Transfer of control between an automated system and a human operator, bounded by human reaction time, situational awareness, and mode clarity.", "background": false, "home": "Q5.3", "home_id": "human-oversight" }, { "id": "mlsys.ai-risk-management", "label": "AI risk management", "aliases": [ "NIST AI RMF", "ISO/IEC 42001" ], "definition": "An organizational process to govern, map, measure, and manage risks of AI systems throughout their lifecycle.", "background": false, "home": "Q5.4", "home_id": "governance-regulation" }, { "id": "mlsys.impact-assessment", "label": "Algorithmic impact assessment", "aliases": [], "definition": "A structured pre-deployment analysis of who an AI system could affect, how, and with what severity, used to decide mitigations or whether to deploy.", 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expiry, and invalidation.", "background": false, "home": "T1.1", "home_id": "context-memory" }, { "id": "mlsys.function-calling", "label": "Function calling", "aliases": [ "tool calling", "tool use" ], "definition": "A model emitting a structured invocation of a declared tool, which the harness validates, executes, and returns as an observation.", "background": false, "home": "T1.2", "home_id": "tool-interfaces" }, { "id": "mlsys.model-context-protocol", "label": "Tool discovery protocol", "aliases": [ "Model Context Protocol", "MCP" ], "definition": "An open client-server protocol through which agent hosts discover and invoke tools, resources, and prompts exposed by external servers.", "background": false, "home": "T1.2", "home_id": "tool-interfaces" }, { "id": "mlsys.computer-use", "label": "Computer use", "aliases": [ "GUI agent", "browser agent" ], "definition": "An agent operating graphical software through screenshots or accessibility trees and synthesized keyboard and pointer input.", "background": false, "home": "T1.2", "home_id": "tool-interfaces" }, { "id": "mlsys.agent-harness", "label": "Agent harness", "aliases": [ "agent loop", "agent runtime" ], "definition": "The runtime that repeatedly calls a model, dispatches its tool actions, appends observations, and decides whether the trajectory continues, pauses, or ends.", "background": false, "home": "T1.3", "home_id": "agent-harness" }, { "id": "mlsys.agent-budget", "label": "Agent execution budget", "aliases": [], "definition": "Limits on turns, tokens, wall-clock time, or spend for a trajectory, with stopping rules applied when a limit or completion criterion is reached.", "background": false, "home": "T1.3", "home_id": "agent-harness" }, { "id": "mlsys.approval-gate", "label": "Action approval gate", "aliases": [ "human-in-the-loop approval" ], "definition": "A harness checkpoint that holds a specific proposed action until an authorized human or policy approves it, binding the approval to that exact action.", "background": false, "home": "T1.3", "home_id": "agent-harness" }, { "id": "mlsys.durable-execution", "label": "Durable execution", "aliases": [], "definition": "Recording each workflow step's intent and result in a persistent log so execution resumes after failure without repeating completed steps.", "background": false, "home": "T1.4", "home_id": "durable-execution" }, { "id": "mlsys.deterministic-replay", "label": "Record and replay", "aliases": [ "deterministic replay" ], "definition": "Re-executing a trajectory from its log by substituting recorded model outputs and tool results, to reproduce, debug, or test a divergence.", "background": false, "home": "T1.4", "home_id": "durable-execution" }, { "id": "mlsys.suspend-resume", "label": "Suspend and resume", "aliases": [], "definition": "Persisting a trajectory while it waits on a human, timer, or external event, then resuming with a validated state and authority.", "background": false, "home": "T1.4", "home_id": 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of a kernel into grids of thread blocks and warps that share on-chip memory and synchronize at defined scopes.", "background": false, "home": "S2.3", "home_id": "gpu-programming" }, { "id": "mlsys.memory-coalescing", "label": "Memory coalescing and bank conflicts", "aliases": [ "coalesced access" ], "definition": "Access patterns that merge a warp's loads into few memory transactions, or that serialize on shared-memory banks.", "background": false, "home": "S2.3", "home_id": "gpu-programming" }, { "id": "mlsys.occupancy", "label": "Occupancy and latency hiding", "aliases": [ "occupancy" ], "definition": "The ratio of resident to maximum warps, and how oversubscribing threads hides memory latency at the cost of registers and shared memory per thread.", "background": false, "home": "S2.3", "home_id": "gpu-programming" }, { "id": "mlsys.async-data-movement", "label": "Asynchronous data movement", "aliases": [ "warp specialization", "TMA" ], "definition": "Hardware-assisted bulk copies into on-chip memory and producer-consumer warp roles that overlap loads with matrix-engine compute.", "background": false, "home": "S2.3", "home_id": "gpu-programming" }, { "id": "mlsys.matrix-engine-programming", "label": "Matrix-engine programming", "aliases": [ "tensor core programming", "MMA" ], "definition": "Issuing tile-level matrix-multiply-accumulate instructions with the layouts, fragment shapes, and precisions a tensor-core-class unit requires.", "background": false, "home": "S2.3", "home_id": "gpu-programming" }, { "id": "mlsys.embedding-table-sharding", "label": "Embedding-table sharding", "aliases": [ "DLRM sharding" ], "definition": "Partitioning very large sparse embedding tables row-, column-, or table-wise across devices, usually model-parallel beside data-parallel dense layers.", "background": false, "home": "S3.1", "home_id": "parallelism" }, { "id": "mlsys.embedding-cache", "label": "Embedding cache and tiering", "aliases": [ "embedding offload" ], "definition": "Keeping frequently accessed embedding rows in accelerator memory and cold rows in host memory or SSD, exploiting the skew of lookups.", "background": false, "home": "S1.2", "home_id": "memory-systems" }, { "id": "mlsys.parameter-server", "label": "Parameter server", "aliases": [ "PS architecture" ], "definition": "An architecture in which workers pull and push model parameters from sharded server processes, common for sparse recommendation models and asynchronous training.", "background": false, "home": "S3.1", "home_id": "parallelism" }, { "id": "mlsys.ranking-funnel", "label": "Multi-stage retrieval and ranking", "aliases": [ "ranking funnel", "candidate generation" ], "definition": "Candidate generation followed by progressively costlier ranking stages under one latency budget, the dominant architecture for recommendation and search.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.multimodal-model", "label": "Multimodal model", "aliases": [ "vision-language model", "VLM" ], "definition": "A model that consumes or produces several modalities, such as text, images, audio, or video, through modality encoders feeding a shared backbone.", "background": false, "home": "F2.1", "home_id": "model-families" }, { "id": "mlsys.modality-encoder", "label": "Modality encoder", "aliases": [ "visual tokens", "vision encoder" ], "definition": "A network that turns images, audio, or video into token sequences for a shared backbone; visual tokens often dominate prefill cost and KV size.", "background": false, "home": "F2.2", "home_id": "architecture-blocks" }, { "id": "mlsys.encoder-disaggregation", "label": "Multimodal encoder disaggregation", "aliases": [ "encode-prefill-decode disaggregation" ], "definition": "Scheduling modality encoders separately from prefill and decode and caching encoder outputs so image or audio inputs do not stall token generation.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": 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the busiest axis maps to the fastest interconnect.", "background": false, "home": "S3.2", "home_id": "parallelism-planning" }, { "id": "mlsys.function-transformations", "label": "Composable function transformations", "aliases": [ "grad/vmap/jit", "functional transformations" ], "definition": "Higher-order operations that map pure array functions to new functions, such as differentiation, vectorization, compilation, and sharding, and compose in any order.", "background": false, "home": "S2.1", "home_id": "programming-models" }, { "id": "mlsys.task-actor-runtime", "label": "Distributed task and actor runtime", "aliases": [ "actor model runtime", "Ray-style runtime" ], "definition": "A runtime exposing stateless remote tasks and stateful actors as future-returning calls, scheduled across a cluster with a shared object store and lineage-based fault tolerance.", "background": false, "home": "S2.1", "home_id": "programming-models" }, { "id": "mlsys.single-vs-multi-controller", "label": "Single- vs. multi-controller execution", "aliases": [], "definition": "Whether one coordinating process issues work to all devices or every host runs the same program independently, trading orchestration flexibility against dispatch scalability.", "background": false, "home": "S2.1", "home_id": "programming-models" }, { "id": "mlsys.analytical-performance-model", "label": "Analytical performance model", "aliases": [], "definition": "A closed-form model composing compute, memory, communication, and queueing terms to predict a workload's time, memory, and cost on a configuration without running it.", "background": false, "home": "F3.1", "home_id": "roofline-locality" }, { "id": "mlsys.performance-simulation", "label": "Performance simulation", "aliases": [ "system simulation" ], "definition": "Predicting system behavior by executing a model of compute, memory, network, or scheduling events at a chosen granularity, trading simulation speed against detail.", "background": false, "home": 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decoding, with error-compensating or activation-aware rounding.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.activation-outliers", "label": "Activation outliers", "aliases": [ "outlier features", "SmoothQuant" ], "definition": "Rare, large-magnitude activation channels in large transformers that inflate quantization ranges, handled by mixed-precision decomposition or by migrating scale into weights.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.kv-cache-quantization", "label": "KV-cache quantization", "aliases": [ "KV quantization" ], "definition": "Storing attention keys and values at reduced precision, often with per-channel keys and per-token values, to raise batch size or context length under fixed memory.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.nm-sparsity", "label": "N:M structured sparsity", "aliases": [ "2:4 sparsity", "semi-structured sparsity" ], "definition": "A fine-grained pattern keeping at most N nonzero values in every group of M, such as 2:4, that matrix engines can skip for real speedup.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.kv-cache-offload", "label": "KV-cache offloading and tiering", "aliases": [ "KV offload", "KV tiering" ], "definition": "Moving KV-cache blocks from accelerator memory to host memory or SSD and reloading them on reuse, extending cache capacity at transfer bandwidth and latency cost.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": "mlsys.kv-cache-transfer", "label": "KV-cache transfer", "aliases": [ "KV migration" ], "definition": "Shipping KV state between instances or nodes, as in disaggregated prefill and decode or cross-instance prefix reuse, overlapped with compute to hide network latency.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": "mlsys.compound-ai-system", "label": "Compound AI 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proximity graph, often hierarchical, searched by greedy traversal for high recall at high memory cost.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.quantized-vector-index", "label": "Partitioned and quantized vector index", "aliases": [ "IVF-PQ", "product quantization" ], "definition": "An index that clusters vectors into partitions probed selectively and compresses them with product quantization, trading recall for memory footprint and scan cost.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.filtered-vector-search", "label": "Filtered vector search", "aliases": [ "predicate-constrained ANN" ], "definition": "Nearest-neighbor search restricted to records satisfying metadata predicates, where naive pre- or post-filtering loses recall or cost under selective filters.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.index-freshness", "label": "Vector index freshness", "aliases": [ "streaming ANN updates" ], "definition": "The delay and recall cost of reflecting inserts, updates, and deletes in an ANN index, managed by incremental insertion, delta indexes, tombstones, and periodic rebuilds.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.numerical-nondeterminism", "label": "Numerical nondeterminism", "aliases": [], "definition": "Run-to-run variation in floating-point results because non-associative reductions change order with atomics, kernel selection, parallel layout, batch composition, or hardware.", "background": false, "home": "S2.7", "home_id": "numerics" }, { "id": "mlsys.batch-invariance", "label": "Batch invariance", "aliases": [ "batch-invariant kernels" ], "definition": "A kernel property that each request's output is bitwise independent of batch size and co-batched requests, required for deterministic serving and matching training to inference numerics.", "background": false, "home": "S2.7", "home_id": "numerics" 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"A long-running kernel that stays resident on the device and executes many operators, or a whole forward pass, from an in-kernel work queue.", "background": false, "home": "S2.4", "home_id": "ml-compilers" }, { "id": "mlsys.graph-superoptimization", "label": "Tensor graph superoptimization", "aliases": [ "graph substitution", "TASO-style superoptimization" ], "definition": "Automatically generating and verifying semantics-preserving rewrites of tensor computation graphs or kernels, then searching the rewritten space under a cost model.", "background": false, "home": "S2.4", "home_id": "ml-compilers" }, { "id": "mlsys.llm-training-framework", "label": "Large-model training framework", "aliases": [ "Megatron-LM", "DeepSpeed", "distributed training library" ], "definition": "A library that composes data, tensor, pipeline, and sharded-state parallelism with memory optimizations behind one training configuration for models exceeding one device.", "background": false, "home": "S2.1", "home_id": "programming-models" }, { "id": "mlsys.cloud-service-model", "label": "Cloud service models", "aliases": [ "IaaS", "PaaS", "SaaS", "managed ML service" ], "definition": "Divisions of responsibility between cloud provider and customer, from rented virtual infrastructure to managed platforms to hosted model APIs.", "background": false, "home": "S4.5", "home_id": "capacity-provisioning" }, { "id": "mlsys.lakehouse", "label": "Data warehouse and lakehouse", "aliases": [ "data warehouse", "data lake", "lakehouse" ], "definition": "Analytical stores of governed tables queried with SQL; a lakehouse adds transactional table formats over open files in object storage.", "background": false, "home": "P1.4", "home_id": "data-pipelines" }, { "id": "mlsys.training-power-swings", "label": "Synchronous training power swings", "aliases": [ "training power fluctuations", "training load swings" ], "definition": "Rapid cluster-wide swings in power draw as synchronized accelerators alternate between compute and communication or stall together at checkpoints and failures.", "background": false, "home": "S4.1", "home_id": "datacenter-facilities" }, { "id": "mlsys.facility-energy-storage", "label": "UPS and battery energy storage", "aliases": [ "UPS", "uninterruptible power supply", "BESS" ], "definition": "Facility batteries that carry load through outages until generators start and can also buffer fast load swings or shave demand peaks.", "background": false, "home": "S4.1", "home_id": "datacenter-facilities" }, { "id": "mlsys.grid-interconnection", "label": "Grid interconnection", "aliases": [ "grid connection", "utility interconnection" ], "definition": "The utility connection that fixes how much power a site can draw; its capacity and multi-year lead times increasingly decide where AI datacenters are built.", "background": false, "home": "S4.1", "home_id": "datacenter-facilities" }, { "id": "mlsys.on-site-generation", "label": "On-site power generation", "aliases": [ 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"background": false, "home": "P1.3", "home_id": "data-quality" }, { "id": "mlsys.data-governance", "label": "Data governance", "aliases": [ "data stewardship" ], "definition": "Policies, roles, and controls that decide who may collect, access, use, and share which data, for which purposes, and under what accountability.", "background": false, "home": "P1.5", "home_id": "data-lineage" }, { "id": "mlsys.data-consent", "label": "Consent and purpose limitation", "aliases": [ "purpose limitation", "data-use consent" ], "definition": "Restricting collection and use of personal data to purposes the data subject agreed to or a legal basis allows, recorded so downstream uses can be checked.", "background": false, "home": "P1.5", "home_id": "data-lineage" }, { "id": "mlsys.exploratory-data-analysis", "label": "Exploratory data analysis", "aliases": [ "EDA", "data profiling" ], "definition": "Summarizing and plotting distributions, correlations, and anomalies in a dataset before modeling to inform cleaning, feature design, and validation rules.", "background": false, "home": "P1.3", "home_id": "data-quality" }, { "id": "mlsys.model-visualization", "label": "Model and prediction visualization", "aliases": [ "training dashboards", "embedding projection", "confusion-matrix plots" ], "definition": "Plotting training curves, activations, embeddings, and prediction errors such as confusion matrices to inspect what a model learned and where it fails.", "background": false, "home": "P3.5", "home_id": "ml-testing" }, { "id": "mlsys.model-selection", "label": "Model selection", "aliases": [ "model development" ], "definition": "Choosing among candidate model families, architectures, and hyperparameters by validated performance under the system's accuracy, cost, and latency constraints.", "background": false, "home": "P2.4", "home_id": "experiment-management" }, { "id": "mlsys.pretraining", "label": "Pretraining", "aliases": [ "base-model pretraining", "pretraining run" ], "definition": "Training a model from scratch on a large, broad corpus with a self-supervised objective to produce a general base model for later adaptation.", "background": false, "home": "P2.1", "home_id": "learning-paradigms" }, { "id": "mlsys.causal-lm", "label": "Causal language modeling", "aliases": [ "next-token prediction", "autoregressive language modeling" ], "definition": "Training a model to predict each token from the tokens before it, maximizing the likelihood of text under a left-to-right factorization.", "background": false, "home": "P2.1", "home_id": "learning-paradigms" }, { "id": "mlsys.gan", "label": "Generative adversarial network", "aliases": [ "GAN" ], "definition": "A generator and a discriminator trained against each other, so the generator learns to produce samples the discriminator cannot tell apart from data.", "background": false, "home": "F2.1", "home_id": "model-families" }, { "id": "mlsys.mode-collapse", "label": "Mode collapse", "aliases": [], "definition": "A generative-training failure in which the model covers only a few modes of the data distribution and produces low-diversity samples.", "background": false, "home": "P2.2", "home_id": "training-optimization" }, { "id": "mlsys.perplexity", "label": "Perplexity", "aliases": [], "definition": "The exponentiated average negative log-likelihood per token of held-out text, measuring how well a language model predicts that text.", "background": false, "home": "P3.4", "home_id": "generative-agent-eval" }, { "id": "mlsys.reference-match-scoring", "label": "Reference-match scoring", "aliases": [ "exact match", "token-overlap F1", "BLEU", "ROUGE" ], "definition": "Scoring a generated output by exact or token-overlap agreement with reference answers; cheap and reproducible but blind to valid answers phrased differently.", "background": false, "home": "P3.4", "home_id": "generative-agent-eval" }, { "id": "mlsys.error-analysis", "label": "Error analysis", "aliases": [], "definition": "Inspecting a model's mistakes, grouping them into categories, and ranking categories by frequency and fixability to decide the next data or model change.", "background": false, "home": "P3.5", "home_id": "ml-testing" }, { "id": "mlsys.training-diagnostics", "label": "Training diagnostics", "aliases": [ "overfit-one-batch test", "learning-curve diagnosis" ], "definition": "Checks such as overfitting a tiny batch, reading learning curves, and tracking gradient and activation statistics that localize bugs in training code, data, and configuration.", "background": false, "home": "P3.5", "home_id": "ml-testing" }, { "id": "mlsys.keyed-predictions", "label": "Request correlation keys", "aliases": [ "keyed predictions", "pass-through keys" ], "definition": "Passing a client-supplied key through inference unchanged so outputs of batch or asynchronous requests can be matched to their inputs regardless of order.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.stateless-serving-function", "label": "Stateless model serving", "aliases": [ "stateless serving function" ], "definition": "Exporting a model as a stateless function from request inputs to prediction, so any replica can serve any request and replicas scale horizontally.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.model-gateway", "label": "Model gateway", "aliases": [ "LLM gateway", "AI gateway" ], "definition": "A proxy in front of one or more model providers that centralizes authentication, routing, rate limits, fallbacks, and usage logging for model calls.", "background": false, "home": "P4.2", "home_id": "compound-ai" }, { "id": "mlsys.semantic-cache", "label": "Semantic cache", "aliases": [ "semantic caching" ], "definition": "Returning a stored model response when a new request is close in embedding space to an earlier one, trading answer fidelity for latency and cost.", "background": false, "home": "P4.2", "home_id": "compound-ai" }, { "id": "mlsys.glue-code", "label": "Glue code", "aliases": [], "definition": "Supporting code written to move data into and out of general-purpose ML packages, often far larger than the model code and binding the system to package specifics.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.pipeline-jungle", "label": "Pipeline jungle", "aliases": [], "definition": "Data preparation grown incrementally into a tangle of scrapes, joins, and intermediate outputs that is hard to test, trace, and change.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.build-vs-buy", "label": "Build-versus-buy decision", "aliases": [ "build vs. buy", "MLOps tool selection" ], "definition": "Choosing whether to build an ML platform component in house, adopt open source, or buy a managed service, weighing fit, cost, lock-in, and team capacity.", "background": false, "home": "P5.6", "home_id": "ml-platforms" }, { "id": "mlsys.user-feedback", "label": "User feedback collection", "aliases": [ "explicit feedback", "implicit feedback" ], "definition": "Capturing explicit signals such as ratings and corrections and implicit signals such as clicks, edits, and abandonment, for use as labels or quality signals.", "background": false, "home": "P5.3", "home_id": "drift-retraining" }, { "id": "mlsys.human-ai-interaction", "label": "Human-AI interaction design", "aliases": [ "human-centered AI", "human-AI guidelines" ], "definition": "Designing how a system conveys its capabilities, uncertainty, and errors to users and lets them correct, override, or dismiss its outputs.", "background": false, "home": "Q5.3", "home_id": "human-oversight" }, { "id": "mlsys.demand-response", "label": "Datacenter demand response", "aliases": [ "flexible datacenter load", "grid-interactive datacenter" ], "definition": "Shifting, shedding, or capping datacenter power in response to grid signals, made practical by deferrable training and batch work.", "background": false, "home": "Q6.1", "home_id": "energy-carbon" }, { "id": "mlsys.prompt-engineering", "label": "Prompt engineering", "aliases": [ "prompt design" ], "definition": "Designing instructions, examples, and input layout for a model call to elicit desired behavior without changing the model's weights.", "background": false, "home": "P2.5", "home_id": "fine-tuning" }, { "id": "mlsys.in-context-learning", "label": "In-context learning", "aliases": [ "few-shot prompting", "zero-shot prompting" ], "definition": "A pretrained model performing a task from instructions or examples supplied in its input at inference time, with no weight updates.", "background": false, "home": "P2.5", "home_id": "fine-tuning" }, { "id": "mlsys.streaming-asr", "label": "Streaming speech recognition", "aliases": [ "streaming ASR", "on-device speech recognition" ], "definition": "Serving speech recognition incrementally: consuming audio in chunks and emitting partial transcripts within a fixed lag, with encoder state carried between chunks.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.voice-pipeline", "label": "Voice interaction pipeline", "aliases": [ "voice agent", "ASR-LLM-TTS cascade", "speech-to-speech model" ], "definition": "Serving a spoken interaction as one latency-budgeted pipeline of streaming speech recognition, a language model, and speech synthesis, with turn detection and interruption handling between stages.", "background": false, "home": "P4.1", "home_id": "serving-architecture" }, { "id": "mlsys.kv-cache-eviction", "label": "KV-cache eviction", "aliases": [ "KV token eviction", "KV-cache sparsification", "heavy-hitter retention" ], "definition": "Retaining only a selected subset of cached key-value entries, chosen by attention scores, recency, or learned importance, to bound KV memory during long generation.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": "mlsys.dynamic-sparse-attention", "label": "Dynamic sparse attention", "aliases": [ "content-adaptive sparse attention", "input-dependent sparse attention" ], "definition": "Attention that computes only the query-key blocks predicted important for each input at runtime, rather than a fixed pattern, to cut long-context cost.", "background": false, "home": "F2.4", "home_id": "efficient-sequence-models" }, { "id": "mlsys.cpu-gpu-hybrid-inference", "label": "CPU-GPU hybrid inference", "aliases": [ "host-assisted inference", "CPU offloaded attention" ], "definition": "Splitting inference so the host CPU computes some operations, such as attention or rarely active neurons, from host memory when model or KV state exceeds accelerator memory.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": "mlsys.heterogeneous-accelerator-execution", "label": "Heterogeneous-accelerator training and serving", "aliases": [ "heterogeneous GPU clusters", "mixed-generation GPU training" ], "definition": "Partitioning one training or serving job across accelerators of different types or generations, sizing stages and batch shares to each device's compute, memory, and links.", "background": false, "home": "S3.2", "home_id": "parallelism-planning" }, { "id": "mlsys.graph-neural-network", "label": "Graph neural network", "aliases": [ "GNN", "message-passing neural network" ], "definition": "Neural network that computes node, edge, or graph representations by repeatedly aggregating features from each node's neighbors along graph edges.", "background": false, "home": "F2.1", "home_id": "model-families" }, { "id": "mlsys.neighbor-sampling", "label": "Neighbor sampling", "aliases": [ "graph sampling", "mini-batch graph sampling" ], "definition": "Building GNN mini-batches from sampled subsets of each node's multi-hop neighborhood, bounding the exponential neighbor growth that makes full-neighborhood computation exceed memory.", "background": false, "home": "S2.6", "home_id": "data-loading" }, { "id": "mlsys.dynamic-resharding", "label": "Dynamic re-parallelization", "aliases": [ "parameter reallocation", "runtime resharding" ], "definition": "Redistributing model weights among devices at runtime so successive phases, such as generation and training or prefill and decode, each run under a better parallel layout.", "background": false, "home": "S3.2", "home_id": "parallelism-planning" }, { "id": "mlsys.activation-sparsity", "label": "Contextual activation sparsity", "aliases": [ "dynamic activation sparsity", "contextual sparsity" ], "definition": "Skipping neurons, heads, or channels whose activations are predicted negligible for the current input, reducing per-token compute and weight reads at inference.", "background": false, "home": "Q1.3", "home_id": "model-compression" }, { "id": "mlsys.non-prefix-kv-reuse", "label": "Non-prefix KV reuse", "aliases": [ "position-independent KV caching", "KV cache blending" ], "definition": "Reusing precomputed KV entries for text chunks appearing at arbitrary positions across requests, selectively recomputing cross-chunk attention instead of the full prefill.", "background": false, "home": "P4.5", "home_id": "llm-inference" }, { "id": "mlsys.ml-aware-scheduling", "label": "ML-aware cluster scheduling", "aliases": [ "deep-learning cluster scheduling", "goodput-aware scheduling" ], "definition": "Allocating cluster accelerators using ML job properties, such as scaling curves, iteration time, and parallelism choice, to optimize objectives like job completion time or goodput.", "background": false, "home": "S4.4", "home_id": "cluster-scheduling" }, { "id": "mlsys.adaptive-routing", "label": "Adaptive routing", "aliases": [ "flowlet switching", "packet spraying", "ECMP collision avoidance" ], "definition": "Choosing network paths per packet or flowlet from current congestion, avoiding the hash collisions static ECMP suffers with few large, synchronized training flows.", "background": false, "home": "S4.2", "home_id": "network-fabrics" }, { "id": "mlsys.incast", "label": "Incast congestion", "aliases": [ "incast", "many-to-one congestion" ], "definition": "Congestion that occurs when many senders transmit to one receiver at once, overflowing its switch port buffer and causing queueing, drops, or pause propagation.", "background": false, "home": "S4.2", "home_id": "network-fabrics" }, { "id": "mlsys.context-selection", "label": "Context selection", "aliases": [ "context budget allocation", "tool selection", "working-set selection" ], "definition": "Choosing which instructions, tool descriptions, retrieved items, state, and observations enter a model's limited context at each step, within a token budget.", "background": false, "home": "T1.1", "home_id": "context-memory" }, { "id": "mlsys.context-staleness", "label": "Context staleness", "aliases": [ "stale observations", "context invalidation" ], "definition": "Observations held in an agent's context or memory that no longer reflect the environment after later actions or external changes, yet still steer decisions unless invalidated.", "background": false, "home": "T1.1", "home_id": "context-memory" }, { "id": "mlsys.multi-hop-retrieval", "label": "Iterative multi-hop retrieval", "aliases": [ "agentic retrieval", "multi-hop RAG" ], "definition": "Retrieval that issues successive queries, each formulated from previously retrieved evidence or reasoning steps, to answer questions no single lookup resolves.", "background": false, "home": "P4.8", "home_id": "retrieval-rag" }, { "id": "mlsys.capability-based-security", "label": "Capability-based authorization", "aliases": [ "capability tokens", "object capabilities", "attenuated credentials" ], "definition": "Authorization in which holding an unforgeable, scoped reference grants a specific right that can be delegated with attenuated scope and later revoked.", "background": false, "home": "Q3.4", "home_id": "access-isolation" }, { "id": "mlsys.ephemeral-sandbox", "label": "Ephemeral sandbox", "aliases": [ "copy-on-write workspace", "disposable execution environment", "microVM sandbox" ], "definition": "An isolated execution environment created per task from a snapshot, often copy-on-write, and destroyed or reset afterward so no state carries across tasks or tenants.", "background": false, "home": "Q3.4", "home_id": "access-isolation" }, { "id": "mlsys.egress-control", "label": "Network egress control", "aliases": [ "egress filtering", "outbound allowlist" ], "definition": "Restricting which external network destinations a workload or sandbox may contact, typically through default-deny allowlists, to block exfiltration and unintended remote actions.", "background": false, "home": "Q3.4", "home_id": "access-isolation" }, { "id": "mlsys.information-flow-control", "label": "Information-flow control", "aliases": [ "taint tracking", "data-flow labels" ], "definition": "Labeling data with confidentiality and integrity tags and propagating them through computation so policy can block untrusted inputs from steering privileged actions or secrets from leaking.", "background": false, "home": "Q3.4", "home_id": "access-isolation" }, { "id": "mlsys.compounding-error", "label": "Compounding error in sequential decisions", "aliases": [ "covariate shift in imitation", "exposure bias", "horizon error compounding" ], "definition": "Per-step errors of a learned sequential policy or generator push later inputs away from the training distribution, so failure probability grows with horizon length.", "background": false, "home": "P2.1", "home_id": "learning-paradigms" }, { "id": "mlsys.interactive-imitation", "label": "Interactive imitation learning", "aliases": [ "DAgger", "dataset aggregation", "recovery demonstrations" ], "definition": "Imitation learning that queries an expert for corrective labels on states the learner's own policy visits and aggregates them into training data over rounds.", "background": false, "home": "P2.1", "home_id": "learning-paradigms" }, { "id": "mlsys.loss-masking", "label": "Loss masking and token normalization", "aliases": [ "prompt masking", "assistant-only loss" ], "definition": "Excluding prompt, tool-output, and other non-target tokens from the training loss, and choosing how remaining token losses are averaged across turns and sequences.", "background": false, "home": "P2.5", "home_id": "fine-tuning" }, { "id": "mlsys.credit-assignment", "label": "Credit assignment", "aliases": [ "temporal credit assignment", "multi-turn credit assignment" ], "definition": "Attributing a delayed or episode-level reward to the individual actions, turns, or tokens that produced it, so updates reinforce the responsible decisions.", "background": false, "home": "P2.6", "home_id": "post-training" }, { "id": "mlsys.actuator-limits", "label": "Actuator limits", "aliases": [ "torque saturation", "thermal duty limits" ], "definition": "Bounds on the force, torque, speed, and sustained duty an actuator can deliver, set by drive current limits, gearing, and motor heating.", "background": false, "home": "T2.3", "home_id": "planning-control" }, { "id": "mlsys.watchdog-timer", "label": "Watchdog timer", "aliases": [ "heartbeat timeout", "windowed watchdog", "authority-expiry timer" ], "definition": "A timer that a monitored component must refresh within a deadline or window; expiry triggers a defined safe response such as reset or revoking control authority.", "background": false, "home": "T2.4", "home_id": "real-time-systems" }, { "id": "mlsys.closed-loop-evaluation", "label": "Closed-loop policy evaluation", "aliases": [ "closed-loop testing", "interactive policy evaluation" ], "definition": "Evaluating a policy by letting its own actions determine subsequent observations, in simulation or deployment, rather than scoring its predictions against logged data.", "background": false, "home": "Q4.3", "home_id": "verification-validation" }, { "id": "mlsys.event-data-recorder", "label": "Event data recorder", "aliases": [ "black-box recorder", "safety event log", "authority record" ], "definition": "Tamper-evident onboard logging of inputs, decisions, control authority, and safety interventions around events, retained for incident reconstruction and assurance evidence.", "background": false, "home": "Q4.4", "home_id": "safety-cases" }, { "id": "mlsys.decision-accountability", "label": "Decision accountability record", "aliases": [ "decision ownership", "accountability assignment", "approval record" ], "definition": "A record of who authorized each consequential decision, policy, or automated action and on what evidence, so responsibility can be assigned and reviewed later.", "background": false, "home": "Q5.4", "home_id": "governance-regulation" }, { "id": "mlsys.agent-interruption", "label": "Agent interruption and cancellation", "aliases": [ "pause and cancel", "operator interrupt", "agent kill switch" ], "definition": "Operator controls that pause, redirect, cancel, or terminate a running agent at defined boundaries while settling in-flight tool calls into a known state.", "background": false, "home": "T1.3", "home_id": "agent-harness" }, { "id": "mlsys.agent-loop-detection", "label": "Agent loop detection", "aliases": [ "semantic watchdog", "livelock detection", "repetition detection" ], "definition": "Monitoring an agent trajectory for repeated actions, cycling states, or absent progress, and halting or escalating when the agent appears stuck.", "background": false, "home": "T1.3", "home_id": "agent-harness" }, { "id": "mlsys.task-and-motion-planning", "label": "Task and motion planning", "aliases": [ "TAMP" ], "definition": "Planning that jointly searches discrete symbolic action sequences and continuous motions, accepting a task plan only when each step admits a feasible motion.", "background": false, "home": "T2.3", "home_id": "planning-control" }, { "id": "mlsys.multi-object-tracking", "label": "Multi-object tracking", "aliases": [ "MOT", "data association", "track management" ], "definition": "Maintaining identities and states of multiple moving objects over time by associating new detections with existing tracks and starting or ending tracks.", "background": false, "home": "T2.2", "home_id": "state-estimation" }, { "id": "mlsys.change-impact-analysis", "label": "Safety change-impact analysis", "aliases": [ "impact analysis", "safety-case maintenance" ], "definition": "Determining which hazards, safety-case claims, and evidence a change to a model, software, data, or operating domain invalidates, and what must be re-verified.", "background": false, "home": "Q4.4", "home_id": "safety-cases" }, { "id": "mlsys.sparse-tensor-kernels", "label": "Sparse tensor kernels", "aliases": [ "SpMM", "SDDMM", "sparse convolution" ], "definition": "Kernels such as sparse-dense matrix multiply, sampled dense-dense multiply, and sparse convolution that compute over compressed nonzero structure, trading irregular access for skipped work.", "background": false, "home": "S2.2", "home_id": "tensor-operators" }, { "id": "mlsys.local-sgd", "label": "Local SGD", "aliases": [ "periodic model averaging", "DiLoCo-style training", "low-communication training" ], "definition": "Data-parallel training in which workers take several local optimizer steps between parameter synchronizations, cutting communication frequency for low-bandwidth or geo-distributed settings.", "background": false, "home": "S3.3", "home_id": "collectives" }, { "id": "mlsys.expert-placement", "label": "Expert replication and placement", "aliases": [ "expert load balancing", "redundant experts" ], "definition": "Assigning mixture-of-experts experts to devices, and replicating frequently routed ones, to balance per-device token load under skewed routing.", "background": false, "home": "S3.2", "home_id": "parallelism-planning" }, { "id": "mlsys.memory-efficient-optimizer", "label": "Memory-efficient optimizer", "aliases": [ "low-rank optimizer state", "8-bit optimizer" ], "definition": "Optimizers that shrink per-parameter state through factorization, low-rank projection, or low-precision storage, cutting the memory adaptive methods such as Adam otherwise require.", "background": false, "home": "P2.3", "home_id": "training-memory" }, { "id": "mlsys.execution-trace", "label": "Operator execution trace", "aliases": [ "execution graph trace", "Chakra trace" ], "definition": "A recorded graph of a training or inference step's compute and communication operators with their dependencies and timings, replayable for simulation and benchmarking.", "background": false, "home": "F3.1", "home_id": "roofline-locality" }, { "id": "mlsys.offline-rl", "label": "Offline reinforcement learning", "aliases": [ "batch reinforcement learning", "offline RL" ], "definition": "Learning a policy from a fixed dataset of previously logged interactions without further environment access, limited by how well the data covers states the policy would visit.", "background": false, "home": "P2.1", "home_id": "learning-paradigms" }, { "id": "mlsys.byzantine-robust-aggregation", "label": "Byzantine-robust aggregation", "aliases": [ "robust gradient aggregation", "Krum" ], "definition": "Combining worker or client updates with robust statistics, such as medians, trimmed means, or Krum, so a bounded fraction of arbitrary malicious updates cannot steer training.", "background": false, "home": "Q3.1", "home_id": "ml-attacks" }, { "id": "mlsys.cluster-manager", "label": "Cluster manager", "aliases": [ "cluster orchestrator", "cluster control plane", "Kubernetes" ], "definition": "The control plane that admits jobs, places their tasks on machines, enforces quotas and priorities, and reconciles running state toward declared intent across a cluster.", "background": false, "home": "S4.4", "home_id": "cluster-scheduling" }, { "id": "mlsys.rebound-effect", "label": "Rebound effect", "aliases": [ "Jevons paradox" ], "definition": "Efficiency gains lower the cost of a service enough that total demand, and with it total energy or resource use, grows, partly or fully offsetting the savings.", "background": false, "home": "Q6.1", "home_id": "energy-carbon" }, { "id": "mlsys.data-cascade", "label": "Data cascade", "aliases": [ "compounding data defects" ], "definition": "Compounding downstream harm from upstream data problems, such as poor collection or labeling, that surface late, often after deployment, and are costly to trace.", "background": false, "home": "P1.3", "home_id": "data-quality" }, { "id": "mlsys.data-valuation", "label": "Data valuation", "aliases": [ "Data Shapley", "data attribution" ], "definition": "Estimating each training example's or data source's contribution to model quality, for example through influence or Shapley-style attribution, to guide selection, pricing, or removal.", "background": false, "home": "P1.2", "home_id": "data-curation" }, { "id": "mlsys.diffusion-policy", "label": "Diffusion policy", "aliases": [ "multimodal action head", "flow-matching policy" ], "definition": "A robot policy that generates actions by iterative denoising, representing multimodal action distributions that regression policies average into invalid in-between actions.", "background": false, "home": "T2.5", "home_id": "robot-learning" }, { "id": "mlsys.sensor-spoofing", "label": "Sensor spoofing", "aliases": [ "transduction attack", "physical sensor attack", "LiDAR spoofing" ], "definition": "Attacks that inject false signals into a sensor's physical channel, such as fabricated LiDAR returns or GPS and acoustic spoofing, so perception receives counterfeit measurements.", "background": false, "home": "Q3.1", "home_id": "ml-attacks" }, { "id": "mlsys.paired-comparison", "label": "Paired comparison and noninferiority testing", "aliases": [ "paired difference test", "noninferiority test" ], "definition": "Comparing two systems on the same items or units and testing per-item differences, including whether a candidate is no worse than a baseline by a preset margin.", "background": false, "home": "P3.1", "home_id": "evaluation-protocols" }, { "id": "mlsys.cpu-execution-architecture", "label": "CPU execution architecture", "aliases": [ "general-purpose CPU", "host CPU", "CPU vector units" ], "definition": "Organization of general-purpose processor cores, SIMD vector units, and multilevel caches that runs host code, preprocessing, classical models, and CPU inference.", "background": false, "home": "S1.1", "home_id": "accelerator-architecture" }, { "id": "mlsys.cpu-threading-numa", "label": "CPU threading and NUMA placement", "aliases": [ "thread affinity", "intra-op and inter-op threads", "NUMA-aware execution" ], "definition": "Configuring thread pools, core affinity, and memory placement across NUMA domains so CPU operators parallelize without oversubscription or remote-memory penalties.", "background": false, "home": "S2.5", "home_id": "runtime-dispatch" }, { "id": "mlsys.dynamic-shapes", "label": 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"Waiting to form a batch can add delay; the net effect depends on queueing and service changes." }, { "source": "mlsys.admission", "relation": "requires", "target": "mlsys.service-capacity", "statement": "admits work against a known", "conditions": "Limits must be expressed in resources relevant to the workload." }, { "source": "mlsys.autoscaling", "relation": "determines", "target": "mlsys.service-capacity", "statement": "adjusts provisioned", "conditions": "Provisioning delay, state transfer, and warm-up affect when capacity becomes useful." }, { "source": "mlsys.memory-capacity", "relation": "limits", "target": "mlsys.kv-cache", "statement": "limits the resident", "conditions": "Size depends on architecture, sequence length, format, and concurrency." }, { "source": "mlsys.paged-kv", "relation": "implements", "target": "mlsys.kv-cache", "statement": "reduces allocation waste in", "conditions": "Block metadata, fragmentation, and compatible sharing determine the resulting capacity." }, { "source": "mlsys.prefix-cache", "relation": "uses", "target": "mlsys.kv-cache", "statement": "reuses compatible", "conditions": "Token prefix, model state, position conventions, and access boundaries must match." }, { "source": "mlsys.memory-bandwidth", "relation": "limits", "target": "mlsys.prefill-decode", "statement": "bounds the decode phase of", "conditions": "The dominant resource depends on architecture, batch, context length, and implementation." }, { "source": "mlsys.speculative-decoding", "relation": "increases", "target": "mlsys.service-capacity", "statement": "can raise effective decode throughput of", "conditions": "Acceptance rate, proposer cost, verification, and workload determine the net gain." }, { "source": "mlsys.slo", "relation": "determines", "target": "mlsys.slo-goodput", "statement": "defines the counting rule for", "conditions": "The counting rule must specify latency, quality, failures, and the workload population." }, { "source": "mlsys.evaluation-split", "relation": "measures", "target": "mlsys.generalization", "statement": "supports unbiased estimates of", "conditions": "The partition and sampling unit must match the intended deployment question." }, { "source": "mlsys.selection-bias", "relation": "increases", "target": "mlsys.metrics", "statement": "inflates reported", "conditions": "Independent final evaluation is needed after selection on noisy scores." }, { "source": "mlsys.calibration", "relation": "enables", "target": "mlsys.selective-prediction", "statement": "supports", "conditions": "Abstention thresholds still require risk and coverage evaluation under the deployment distribution." }, { "source": "mlsys.statistical-uncertainty", "relation": "limits", "target": "mlsys.metrics", "statement": "bounds the precision of conclusions from", "conditions": "Dependence, sample selection, and repeated comparison matter." }, { "source": "mlsys.benchmark", "relation": "measures", "target": "mlsys.tail-latency", 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establish harmfulness by themselves." }, { "source": "mlsys.rollout", "relation": "uses", "target": "mlsys.rollback", "statement": "can use", "conditions": "A rollout can use rollback when acceptance criteria fail; state compatibility and external effects may limit recovery." }, { "source": "mlsys.feedback-loop", "relation": "reduces", "target": "mlsys.data-quality", "statement": "can degrade", "conditions": "Deployed decisions can alter which labels and examples are subsequently observed." }, { "source": "mlsys.retraining", "relation": "requires", "target": "mlsys.artifact-version", "statement": "produces a new", "conditions": "An updated learned artifact must be reevaluated and versioned." }, { "source": "mlsys.observability", "relation": "enables", "target": "mlsys.incident-response", "statement": "supports", "conditions": "Useful diagnosis requires recorded context and trustworthy instrumentation." }, { "source": "mlsys.threat-model", "relation": "determines", "target": 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"mlsys.energy", "relation": "increases", "target": "mlsys.carbon", "statement": "contributes operational emissions to", "conditions": "Conversion depends on operational accounting boundaries and energy-source emissions; embodied emissions are separate." }, { "source": "mlsys.unit-cost", "relation": "requires", "target": "mlsys.metrics", "statement": "requires an acceptance criterion from", "conditions": "Counting attempted work as successful outcomes understates the unit cost." }, { "source": "mlsys.capacity-planning", "relation": "requires", "target": "mlsys.slo", "statement": "is sized against", "conditions": "Provisioning decisions must account for demand variation and resource constraints." }, { "source": "mlsys.context", "relation": "limits", "target": "mlsys.trajectory", "statement": "accumulates demand for", "conditions": "Only selected observations and records fit in a model invocation." }, { "source": "mlsys.planning", "relation": "determines", "target": "mlsys.trajectory", 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authorization policy or prove external-effect correctness." }, { "source": "mlsys.data-acquisition", "relation": "determines", "target": "mlsys.evaluation-split", "statement": "determines the observed population for", "conditions": "Selection and missing outcomes can change the population to which an estimate applies." }, { "source": "mlsys.data-contract", "relation": "determines", "target": "mlsys.data-quality", "statement": "specifies the validity criteria for", "conditions": "A schema check alone cannot establish semantic correctness or task fitness." }, { "source": "mlsys.delayed-outcomes", "relation": "limits", "target": "mlsys.monitoring", "statement": "limits timely conclusions from", "conditions": "Outcome maturation and selective observation require explicit evaluation windows." }, { "source": "mlsys.decision-policy", "relation": "uses", "target": "mlsys.calibration", "statement": "uses outputs assessed by", "conditions": "A probability threshold needs task costs and deployment-distribution evidence as well as calibration." }, { "source": "mlsys.supervised-learning", "relation": "requires", "target": "mlsys.labels", "statement": "uses targets assessed by", "conditions": "The target semantics and observation process must match the intended task." }, { "source": "mlsys.self-supervised-learning", "relation": "determines", "target": "mlsys.loss", "statement": "constructs targets for its own", "conditions": "Targets are derived from the data under a specified objective and corruption or prediction scheme." }, { "source": "mlsys.unsupervised-learning", "relation": "implements", "target": "mlsys.features", "statement": "can produce learned", "conditions": "Learned structure or representations require separate downstream evaluation." }, { "source": "mlsys.reinforcement-learning", "relation": "uses", "target": "mlsys.proxy-objective", "statement": "optimizes behavior against a reward acting as", "conditions": "The reward and interaction model may differ from the desired real-world outcome." }, { "source": "mlsys.imitation-learning", "relation": "causes", "target": "mlsys.distribution-shift", "statement": "induces compounding state-distribution shift", "conditions": "Policy actions change the states visited relative to demonstration data." }, { "source": "mlsys.model-artifact", "relation": "requires", "target": "mlsys.memory-capacity", "statement": "must be resident within", "conditions": "Residency can include parameters, tree structure, support vectors, or stored examples, depending on the model." }, { "source": "mlsys.linear-model", "relation": "determines", "target": "mlsys.model-artifact", "statement": "determines the contents of", "conditions": "Feature transforms and output rules are part of the execution contract." }, { "source": "mlsys.tree-ensemble", "relation": "determines", "target": "mlsys.model-artifact", "statement": "determines the contents of", "conditions": "Tree structures, thresholds, leaf values, and aggregation rules require storage and compatible execution." }, { "source": "mlsys.kernel-method", "relation": "determines", "target": "mlsys.model-artifact", "statement": "determines the size of", "conditions": "Support vectors or an explicit approximation affect the stored state and evaluation cost." }, { "source": "mlsys.nearest-neighbor", "relation": "determines", "target": "mlsys.model-artifact", "statement": "determines the contents of", "conditions": "Stored vectors, metadata, and optional indexes contribute capacity and search cost." }, { "source": "mlsys.failure-model", "relation": "limits", "target": "mlsys.consensus", "statement": "bounds the guarantees of", "conditions": "Fault classes, membership, quorum availability, and timing assumptions bound the agreement claim." }, { "source": "mlsys.failure-model", "relation": "determines", "target": "mlsys.checkpoint", "statement": "determines the recovery requirements for", "conditions": "Recovery semantics must cover the faults and state dependencies actually assumed." }, { "source": "mlsys.backpressure", "relation": "limits", "target": "mlsys.arrival-rate", "statement": "limits offered", "conditions": "The signal must reach producers in time and avoid shifting overload into unbounded buffers." }, { "source": "mlsys.privacy", "relation": "limits", "target": "mlsys.privacy-leakage", "statement": "can violate", "conditions": "Risk depends on the information, recipient, purpose, consent, and declared protections." }, { "source": "mlsys.roofline", "relation": "limits", "target": "mlsys.execution-time", "statement": "implies a lower bound on", "conditions": "For fixed arithmetic work, a throughput ceiling implies a lower execution-time bound. Additional overheads can lengthen actual runtime." }, { "source": "mlsys.execution-time", "relation": "increases", "target": "mlsys.tail-latency", "statement": "contributes to", "conditions": "Execution contributes to each request latency; component quantiles cannot generally be added to obtain an end-to-end quantile." }, { "source": "mlsys.memory-capacity", "relation": "limits", "target": "mlsys.batching", "statement": "limits feasible", "conditions": "Model state, request state, intermediates, and allocator overhead must fit for the selected execution strategy." }, { "source": "mlsys.metrics", "relation": "measures", "target": "mlsys.generalization", "statement": "measure", "conditions": "The metric, sampling protocol, and target distribution determine what the estimate means." }, { "source": "mlsys.metrics", "relation": "determines", "target": "mlsys.rollout", "statement": "gate advancement during", "conditions": "Release criteria and populations must be specified before interpreting candidate results." }, { "source": "mlsys.quantization", "relation": "reduces", "target": "mlsys.memory-bandwidth", "statement": "reduces traffic demand on", "conditions": "Smaller representations can reduce transferred bytes; dequantization and execution support determine total cost." }, { "source": "mlsys.monitoring", "relation": "measures", "target": "mlsys.metrics", "statement": "tracks deployed", "conditions": "Observed task metrics can be delayed, biased, or unavailable; system health alone does not establish task quality." }, { "source": "mlsys.data-parallel", "relation": "increases", "target": "mlsys.training-state", "statement": "replicates, multiplying the total footprint of,", "conditions": "Conventional replicated data parallelism keeps model and update state at each worker; ZeRO and hybrid strategies shard selected components." }, { "source": "mlsys.tensor-parallel", "relation": "uses", "target": "mlsys.matrix-multiply", "statement": "partitions", "conditions": "For tensor decompositions whose operand and reduction partitions require compatible communication and layout." }, { "source": "mlsys.profiling", "relation": "measures", "target": "mlsys.execution-time", "statement": "measures observed", "conditions": "Measurement boundaries, instrumentation, and workload determine the observation; a sample is not a universal time bound." }, { "source": "mlsys.profiling", "relation": "measures", "target": "mlsys.wcet", "statement": "samples, but cannot by itself bound,", "conditions": "A justified worst-case bound requires assumptions and analysis covering the relevant executions and hardware states, beyond observed samples." }, { "source": "mlsys.fairness", "relation": "limits", "target": "mlsys.decision-policy", "statement": "constrains acceptable", "conditions": "The protected populations, outcome criterion, operating conditions, and trade-offs must be stated; one criterion does not establish all fairness goals." }, { "source": "mlsys.threat-model", "relation": "determines", "target": "mlsys.robustness-evaluation", "statement": "selects attack cases for", "conditions": "Tests exercise a declared adversary and interface; passing selected attacks does not establish universal robustness." }, { "source": "mlsys.failure-model", "relation": "limits", "target": "mlsys.incident-response", "statement": "bounds planned recovery in", "conditions": "A design's recovery guarantees are limited to its declared faults and assumptions; operations may also encounter unmodeled incidents." }, { "source": "mlsys.feedback-control", "relation": "uses", "target": "mlsys.state-estimation", "statement": "uses estimated state from", "conditions": "For controllers using estimated state, uncertainty and observation timing matter; other output-feedback designs need not reconstruct full state." }, { "source": "mlsys.dynamics", "relation": "determines", "target": "mlsys.feedback-control", "statement": "selects inputs that shape", "conditions": "The closed-loop response depends on the plant, disturbances, constraints, sensing, and controller implementation." }, { "source": "mlsys.runtime-assurance", "relation": "uses", "target": "mlsys.state-estimation", "statement": "relies on qualified", "conditions": "State-dependent checks need valid observation timing and error bounds; the guarantee remains conditional on those premises." }, { "source": "mlsys.operating-domain", "relation": "determines", "target": "mlsys.robustness-evaluation", "statement": "scopes the relevant conditions for", "conditions": "The intended domain identifies relevant stresses and coupled corners, but finite selected tests do not exhaust continuous operating conditions." }, { "source": "mlsys.service-capacity", "relation": "limits", "target": "mlsys.slo-goodput", "statement": "bounds sustainable", "conditions": "Only for matching work units, counting boundary, workload, operating state, and horizon. A measured throughput is not a universal capacity ceiling." }, { "source": "mlsys.energy", "relation": "increases", "target": "mlsys.unit-cost", "statement": "adds monetary cost to", "conditions": "Convert energy through the applicable tariff or cost model; do not add it again when already included in cloud or facility charges." }, { "source": "mlsys.irreversible-effects", "relation": "limits", "target": "mlsys.rollback", "statement": "can prevent full reversal by", "conditions": "Restoring software or configuration cannot necessarily undo physical, informational, financial, or human consequences already caused." }, { "source": "mlsys.irreversible-effects", "relation": "limits", "target": "mlsys.compensation", "statement": "can remain after", "conditions": "A remedy may address an obligation or consequence without restoring the original world state." }, { "source": "mlsys.runtime-assurance", "relation": "mitigates", "target": "mlsys.irreversible-effects", "statement": "can gate prospective", "conditions": "Only when the mechanism mediates the relevant pre-actuation path with sufficient timing and authority. Detection can occur after an effect, and fallback can itself be irreversible." }, { "source": "mlsys.privacy-leakage", "relation": "causes", "target": "mlsys.irreversible-effects", "statement": "can create", "conditions": "Disclosed information may be copied or retained outside the system's control, so subsequent deletion cannot guarantee full retraction." }, { "source": "mlsys.human-oversight", "relation": "limits", "target": "mlsys.authorization", "statement": "can constrain", "conditions": "People need usable evidence, sufficient time, and effective authority; a nominal approval step alone does not establish meaningful control." }, { "source": "mlsys.neural-network", "relation": "determines", "target": "mlsys.model-artifact", "statement": "determines the contents of", "conditions": "Execution requires compatible parameters, operators, and configuration. Training state can include additional data not needed for inference." }, { "source": "mlsys.metrics", "relation": "measures", "target": "mlsys.hallucination", "statement": "can assess support for", "conditions": "For tasks requiring factual or source-grounded assertions, evaluate support in addition to fluency or schema validity; not every task has the same truth criterion." }, { "source": "mlsys.dataset-storage-layout", "relation": "determines", "target": "mlsys.data-ingestion", "statement": "sets the I/O required by", "conditions": "Effects depend on access pattern, projection/filtering, encoding, storage medium, and caching; a layout does not guarantee higher throughput." }, { "source": "mlsys.schema-evolution", "relation": "requires", "target": "mlsys.data-contract", "statement": "requires compatibility rules in", "conditions": "Contracts must identify producers/consumers, field meaning, allowed changes, defaults, and migration expectations; format-supported changes need not preserve application semantics." }, { "source": "mlsys.data-ingestion", "relation": "requires", "target": "mlsys.backpressure", "statement": "can require", "conditions": "When offered record arrival exceeds sustainable processing or downstream capacity, the chosen ingestion system needs an explicit overload policy; not all sources permit slowing producers." }, { "source": "mlsys.point-in-time", "relation": "requires", "target": "mlsys.data-ingestion", "statement": "requires event-time-preserving", "conditions": "When downstream features or evaluation depend on event-time correctness, ingestion/availability timestamps and late-data policies must be retained; arrival order alone is insufficient." }, { "source": "mlsys.integration-testing", "relation": "requires", "target": "mlsys.test-oracle", "statement": "requires expectations from a", "conditions": "The oracle may be partial, probabilistic, or human-assisted and only covers declared properties and tested cases." }, { "source": "mlsys.integration-testing", "relation": "detects", "target": "mlsys.train-serve-skew", "statement": "can expose", "conditions": "Tests compare relevant preprocessing and model interfaces across representative paths; untested distributions or deployment differences can remain." }, { "source": "mlsys.integration-testing", "relation": "measures", "target": "mlsys.dependency-environment", "statement": "tests compatibility within", "conditions": "Test environments must represent the target packages, binaries, resources, and configuration; passing one environment does not validate all others." }, { "source": "mlsys.failure-model", "relation": "determines", "target": "mlsys.failure-injection", "statement": "selects candidate scenarios for", "conditions": "Injected faults instantiate selected assumptions; realistic timing, combinations, observation criteria, and containment must be declared." }, { "source": "mlsys.failure-injection", "relation": "measures", "target": "mlsys.incident-response", "statement": "exercises", "conditions": "A controlled scenario can test detection and response paths; success does not demonstrate preparedness for every incident." }, { "source": "mlsys.failure-injection", "relation": "requires", "target": "mlsys.test-oracle", "statement": "requires observable acceptance criteria from", "conditions": "Criteria may evaluate latency, error rates, recovery behavior, or invariants; injecting a fault without assessing outcomes is not sufficient evidence." }, { "source": "mlsys.experiment-reproducibility", "relation": "requires", "target": "mlsys.dependency-environment", "statement": "requires a pinned", "conditions": "Package identity alone is insufficient: hardware, data, seeds, nondeterministic operations, external services, and acceptance criteria may also matter." }, { "source": "mlsys.artifact-version", "relation": "enables", "target": "mlsys.experiment-reproducibility", "statement": "identifies the exact inputs for", "conditions": "Reproduction requires resolvable versions and the execution procedure/environment; version labels alone do not reproduce results." }, { "source": "mlsys.workflow-orchestration", "relation": "produces", "target": "mlsys.lineage", "statement": "can record execution evidence as", "conditions": "Only when actual input/output artifacts, parameters, steps, and execution identities are captured; orchestration alone does not establish complete provenance." }, { "source": "mlsys.workflow-orchestration", "relation": "requires", "target": "mlsys.idempotency", "statement": "requires retry-safe steps through", "conditions": "When steps can be retried after ambiguous completion, repeated external effects need explicit handling; idempotency is one option rather than a universal orchestration guarantee." }, { "source": "mlsys.benchmark", "relation": "requires", "target": "mlsys.experiment-reproducibility", "statement": "requires", "conditions": "The benchmark protocol must state tolerances, repetitions, environment, and workload; reproducibility does not establish external validity." }, { "source": "mlsys.memory-hierarchy", "relation": "enables", "target": "mlsys.locality", "statement": "provides storage levels for", "conditions": "The algorithm, placement, and implementation determine which levels actually serve an access; effective rates depend on contention and access pattern." }, { "source": "mlsys.host-device-interconnect", "relation": "increases", "target": "mlsys.execution-time", "statement": "can add transfer delay to", "conditions": "When the measured execution boundary includes traffic crossing the host-device path, transport, topology, contention, reuse, and overlap determine its contribution; fully hidden transfers need not increase end-to-end time." }, { "source": "mlsys.accelerator-execution-architecture", "relation": "determines", "target": "mlsys.matrix-multiply", "statement": "constrains supported mappings of", "conditions": "Operations, data types, shapes, and implementation must match the execution resources; peak throughput is not achieved throughput." }, { "source": "mlsys.power-delivery", "relation": "limits", "target": "mlsys.compute-throughput", "statement": "constrains feasible operating points for", "conditions": "Supply limits, voltage integrity, and platform power management can constrain sustained execution; power is distinct from energy." }, { "source": "mlsys.thermal-dynamics", "relation": "increases", "target": "mlsys.execution-time", "statement": "can increase", "conditions": "When thermal controls reduce frequency or service rate or suspend operation, a cold unloaded timing measurement need not characterize sustained or shared operation." }, { "source": "mlsys.thermal-dynamics", "relation": "increases", "target": "mlsys.energy", "statement": "adds cooling overhead to", "conditions": "The system boundary and cooling overhead must be stated; carbon conclusions additionally require energy mix and lifecycle assumptions." }, { "source": "mlsys.sli", "relation": "part-of", "target": "mlsys.slo", "statement": "is the measured quantity in", "conditions": "The indicator's population, window, and aggregation must match the objective's." }, { "source": "mlsys.slo", "relation": "determines", "target": "mlsys.error-budget", "statement": "sets the size of", "conditions": "The objective is stated as a fraction over a defined window." }, { "source": "mlsys.error-budget", "relation": "determines", "target": "mlsys.rollout", "statement": "paces", "conditions": "Teams agree to slow or halt changes when the remaining budget is exhausted." }, { "source": "mlsys.latency-budget", "relation": "limits", "target": "mlsys.tail-latency", "statement": "allocates permitted", "conditions": "Stage latencies compose along a request path whose end-to-end target is fixed." }, { "source": "mlsys.flop-accounting", "relation": "measures", "target": "mlsys.arithmetic-intensity", "statement": "supplies the operation and byte counts for", "conditions": "Operation and byte counts are taken at the same memory boundary." }, { "source": "mlsys.memory-capacity", "relation": "limits", "target": "mlsys.memory-footprint", "statement": "caps", "conditions": "All resident state is counted at its peak, including runtime buffers." }, { "source": "mlsys.capacity-planning", "relation": "uses", "target": "mlsys.workload-trace", "statement": "grounds demand estimates in", "conditions": "The trace is representative of the arrival, size, and mix distribution being provisioned for." }, { "source": "mlsys.flop-accounting", "relation": "part-of", "target": "mlsys.workload", "statement": "estimates the operations and bytes of", "conditions": "Model shapes and input sizes are known." }, { "source": "mlsys.cost-sensitive-decision", "relation": "determines", "target": "mlsys.decision-policy", "statement": "sets thresholds in", "conditions": "Error costs and class priors can be stated for the deployment context." }, { "source": "mlsys.pareto-frontier", "relation": "limits", "target": "mlsys.requirements", "statement": "select an operating point on", "conditions": "Two or more objectives are measured on the same workload and cannot all be optimized at once." }, { "source": "mlsys.transformer", "relation": "uses", "target": "mlsys.attention", "statement": "is built from", "conditions": "The architecture uses attention layers interleaved with feed-forward blocks." }, { "source": "mlsys.recommendation-model", "relation": "uses", "target": "mlsys.embeddings", "statement": "is dominated by lookups in", "conditions": "Categorical features are encoded through large embedding tables." }, { "source": "mlsys.diffusion-model", "relation": "increases", "target": "mlsys.execution-time", "statement": "multiplies by sampling step count", "conditions": "Generation runs the denoising network once per sampling step." }, { "source": "mlsys.convolution", "relation": "increases", "target": "mlsys.arithmetic-intensity", "statement": "typically raises", "conditions": "Filters are reused across many spatial positions within one kernel." }, { "source": "mlsys.recurrence", "relation": "increases", "target": "mlsys.serial-fraction", "statement": "adds sequential dependence to", "conditions": "Each step depends on the previous hidden state, preventing parallelism across time steps." }, { "source": "mlsys.normalization-layer", "relation": "increases", "target": "mlsys.numerical-stability", "statement": "improves", "conditions": "Activation statistics would otherwise drift with depth or batch composition." }, { "source": "mlsys.mixture-of-experts", "relation": "reduces", "target": "mlsys.active-parameters", "statement": "reduces relative to total parameters", "conditions": "Each token is routed to only a small subset of experts." }, { "source": "mlsys.mixture-of-experts", "relation": "requires", "target": "mlsys.memory-capacity", "statement": "still requires storing all experts within", "conditions": "Every expert must be resident somewhere in the system even if inactive for a token." }, { "source": "mlsys.expert-routing", "relation": "determines", "target": "mlsys.all-to-all", "statement": "determines token traffic in", "conditions": "Experts are placed on different devices from the tokens routed to them." }, { "source": "mlsys.expert-capacity", "relation": "limits", "target": "mlsys.expert-routing", "statement": "bounds assignments made by", "conditions": "A fixed per-expert buffer is allocated per batch." }, { "source": "mlsys.load-balancing-loss", "relation": "determines", "target": "mlsys.expert-routing", "statement": "regularizes toward balance", "conditions": "The auxiliary loss weight is large enough to counter router collapse without dominating the task loss." }, { "source": "mlsys.active-parameters", "relation": "determines", "target": "mlsys.flop-accounting", "statement": "determines per-token compute in", "conditions": "Compute is counted for the experts actually activated rather than all stored parameters." }, { "source": "mlsys.early-exit", "relation": "reduces", "target": "mlsys.execution-time", "statement": "reduces average, and makes input-dependent,", "conditions": "Exit criteria differ across inputs, so latency varies with input difficulty." }, { "source": "mlsys.context", "relation": "increases", "target": "mlsys.kv-cache", "statement": "scales the size of", "conditions": "Autoregressive decoding retains keys and values for every prior position." }, { "source": "mlsys.context", "relation": "determines", "target": "mlsys.attention", "statement": "raises the pairwise score cost of", "conditions": "Full attention compares every query with every key." }, { "source": "mlsys.sliding-window-attention", "relation": "enables", "target": "mlsys.context", "statement": "makes long contexts affordable by linearizing attention cost over", "conditions": "Positions attend only within a window or fixed sparse pattern, possibly at a quality cost on long-range tasks." }, { "source": "mlsys.linear-attention", "relation": "reduces", "target": "mlsys.kv-cache", "statement": "replaces with a fixed-size state", "conditions": "The attention kernel is approximated by a feature map that permits a recurrent formulation." }, { "source": "mlsys.state-space-model", "relation": "specializes", "target": "mlsys.recurrence", "statement": "is a parallel-trainable form of", "conditions": "The state update is linear (or selectively gated) so training can use scans or convolutions." }, { "source": "mlsys.grouped-query-attention", "relation": "reduces", "target": "mlsys.kv-cache", "statement": "reduces the size of", "conditions": "Fewer key-value heads are stored than query heads." }, { "source": "mlsys.context-extension", "relation": "uses", "target": "mlsys.position-encoding", "statement": "rescales or interpolates", "conditions": "The model uses a position encoding, such as rotary embeddings, that can be rescaled or interpolated." }, { "source": "mlsys.weight-serialization", "relation": "part-of", "target": "mlsys.model-artifact", "statement": "persists the parameters of", "conditions": "The artifact's weights are persisted for later loading." }, { "source": "mlsys.model-exchange-format", "relation": "enables", "target": "mlsys.compilation", "statement": "provides input to", "conditions": "The target runtime or compiler supports the exchanged operator set." }, { "source": "mlsys.model-export", "relation": "produces", "target": "mlsys.model-exchange-format", "statement": "can emit", "conditions": "The program's operators and control flow are expressible in the format." }, { "source": "mlsys.integration-testing", "relation": "measures", "target": "mlsys.model-signature", "statement": "checks callers against", "conditions": "Callers and the artifact agree on input and output names, types, and shapes." }, { "source": "mlsys.ridge-point", "relation": "determines", "target": "mlsys.bound-regime", "statement": "separates", "conditions": "Throughput and bandwidth ceilings are measured for the same device and memory level." }, { "source": "mlsys.prefill-decode", "relation": "determines", "target": "mlsys.bound-regime", "statement": "classifies prefill as compute-bound and decode as memory-bound in", "conditions": "Prefill processes many tokens per weight read while decode processes few." }, { "source": "mlsys.data-movement-energy", "relation": "increases", "target": "mlsys.energy", "statement": "often dominates", "conditions": "Operands are fetched from off-chip memory rather than reused locally." }, { "source": "mlsys.locality", "relation": "reduces", "target": "mlsys.data-movement-energy", "statement": "reduces", "conditions": "Reused data is served from nearer, lower-energy storage levels." }, { "source": "mlsys.amdahls-law", "relation": "uses", "target": "mlsys.serial-fraction", "statement": "parameterizes", "conditions": "The unaccelerated fraction is measured on the same workload as the speedup." }, { "source": "mlsys.amdahls-law", "relation": "limits", "target": "mlsys.strong-scaling", "statement": "bounds speedup under", "conditions": "Problem size is fixed while resources grow." }, { "source": "mlsys.stragglers", "relation": "reduces", "target": "mlsys.parallel-efficiency", "statement": "reduce", "conditions": "Workers synchronize so the slowest determines step time." }, { "source": "mlsys.collectives", "relation": "reduces", "target": "mlsys.parallel-efficiency", "statement": "can reduce", "conditions": "Communication time is exposed rather than overlapped with computation." }, { "source": "mlsys.littles-law", "relation": "uses", "target": "mlsys.arrival-rate", "statement": "relates system occupancy to", "conditions": "The system is stable and averages are taken over a long window." }, { "source": "mlsys.littles-law", "relation": "measures", "target": "mlsys.kv-cache", "statement": "sizes concurrent state held in", "conditions": "Each in-flight request holds cache state for its duration." }, { "source": "mlsys.latency-throughput-curve", "relation": "determines", "target": "mlsys.slo-goodput", "statement": "locates the maximum load meeting", "conditions": "Latency is measured at the percentile the objective specifies." }, { "source": "mlsys.arrival-burstiness", "relation": "increases", "target": "mlsys.queueing", "statement": "increases", "conditions": "Mean utilization is held fixed while inter-arrival variability grows." }, { "source": "mlsys.compute-optimal-allocation", "relation": "requires", "target": "mlsys.scaling-laws", "statement": "is derived from", "conditions": "The fitted law is used within the range of model and data sizes it was fit on." }, { "source": "mlsys.compute-optimal-allocation", "relation": "uses", "target": "mlsys.training-compute-estimate", "statement": "uses", "conditions": "Dense models are trained where the six-operations-per-parameter-per-token approximation holds." }, { "source": "mlsys.compute-optimal-allocation", "relation": "trades-off", "target": "mlsys.unit-cost", "statement": "can trade off against lifecycle", "conditions": "Large inference volume can favor smaller models trained beyond the compute-optimal point." }, { "source": "mlsys.training-compute-estimate", "relation": "specializes", "target": "mlsys.flop-accounting", "statement": "is a special case of", "conditions": "The model is a dense transformer and attention cost is small relative to parameter compute." }, { "source": "mlsys.systolic-array", "relation": "implements", "target": "mlsys.matrix-multiply", "statement": "executes", "conditions": "Operand shapes are tiled to the array dimensions." }, { "source": "mlsys.matrix-unit", "relation": "requires", "target": "mlsys.precision", "statement": "reaches peak throughput only at supported", "conditions": "Operations use input and accumulation formats the unit implements." }, { "source": "mlsys.simt", "relation": "determines", "target": "mlsys.compute-throughput", "statement": "loses achieved throughput under divergent control flow in", "conditions": "Threads in a group take different control-flow paths." }, { "source": "mlsys.dataflow", "relation": "determines", "target": "mlsys.data-movement-energy", "statement": "determines", "conditions": "The accelerator's local storage cannot hold all operands at once." }, { "source": "mlsys.domain-specific-architecture", "relation": "increases", "target": "mlsys.compute-throughput", "statement": "raises, on its target operations,", "conditions": "The target operations dominate the workload the hardware runs." }, { "source": "mlsys.hbm", "relation": "increases", "target": "mlsys.memory-bandwidth", "statement": "provides high", "conditions": "Data resides in device memory rather than host memory." }, { "source": "mlsys.on-chip-sram", "relation": "limits", "target": "mlsys.tiling", "statement": "bounds tile size for", "conditions": "Tiles must fit in on-chip storage for reuse to occur." }, { "source": "mlsys.memory-wall", "relation": "determines", "target": "mlsys.bound-regime", "statement": "shifts workloads toward the memory-bound side of", "conditions": "Peak arithmetic throughput grows faster than memory bandwidth across hardware generations." }, { "source": "mlsys.scale-up-interconnect", "relation": "limits", "target": "mlsys.tensor-parallel", "statement": "bounds the efficient group size of", "conditions": "Tensor-parallel collectives occur every layer and need the highest-bandwidth links." }, { "source": "mlsys.coherent-interconnect", "relation": "increases", "target": "mlsys.memory-capacity", "statement": "can extend", "conditions": "Pooled or attached memory is acceptable at its higher latency." }, { "source": "mlsys.peer-to-peer-transfer", "relation": "uses", "target": "mlsys.host-device-interconnect", "statement": "bypasses host staging on", "conditions": "Devices, drivers, and topology support direct transfers." }, { "source": "mlsys.tdp", "relation": "limits", "target": "mlsys.thermal-dynamics", "statement": "sets the design envelope for", "conditions": "Sustained workloads, rather than brief bursts, determine heat removal needs." }, { "source": "mlsys.dvfs", "relation": "reduces", "target": "mlsys.energy", "statement": "can reduce, at some cost in speed,", "conditions": "Workload phases tolerate lower clocks, such as memory-bound phases." }, { "source": "mlsys.dark-silicon", "relation": "enables", "target": "mlsys.domain-specific-architecture", "statement": "motivates", "conditions": "Power, not transistor count, limits how much of a chip can be active." }, { "source": "mlsys.power-capping", "relation": "reduces", "target": "mlsys.compute-throughput", "statement": "can lower", "conditions": "The cap binds below the power the workload would otherwise draw." }, { "source": "mlsys.microcontroller", "relation": "determines", "target": "mlsys.on-device-memory-budget", "statement": "imposes", "conditions": "Weights and activations must fit in on-chip flash and SRAM." }, { "source": "mlsys.on-device-memory-budget", "relation": "requires", "target": "mlsys.quantization", "statement": "often requires", "conditions": "The full-precision model exceeds flash or SRAM limits." }, { "source": "mlsys.edge-npu", "relation": "requires", "target": "mlsys.precision", "statement": "requires supported low", "conditions": "The accelerator implements only specific integer or reduced-precision formats." }, { "source": "mlsys.heterogeneous-soc", "relation": "requires", "target": "mlsys.placement", "statement": "requires operator-level", "conditions": "Different blocks support different operators, precisions, and power states." }, { "source": "mlsys.energy-per-inference", "relation": "specializes", "target": "mlsys.energy", "statement": "is a per-input case of", "conditions": "The boundary is one inference on one device, including memory and idle overhead." }, { "source": "mlsys.tensor-layout", "relation": "part-of", "target": "mlsys.tensor", "statement": "is metadata carried by", "conditions": "The framework records strides or format metadata with each tensor." }, { "source": "mlsys.autodiff", "relation": "uses", "target": "mlsys.computation-graph", "statement": "traverses", "conditions": "Derivatives are composed in reverse over recorded operations." }, { "source": "mlsys.eager-execution", "relation": "determines", "target": "mlsys.dispatch", "statement": "incurs per-operation", "conditions": "Each operation is launched separately from the host program." }, { "source": "mlsys.tracing-jit", "relation": "enables", "target": "mlsys.compilation", "statement": "enables", "conditions": "Traced shapes and control flow remain valid for later inputs or guards trigger recompilation." }, { "source": "mlsys.io-aware-attention", "relation": "requires", "target": "mlsys.on-chip-sram", "statement": "depends on the capacity of", "conditions": "Tile sizes are chosen to fit on-chip memory." }, { "source": "mlsys.convolution-lowering", "relation": "uses", "target": "mlsys.matrix-multiply", "statement": "maps convolution onto", "conditions": "The im2col-style transformation is used, at the cost of duplicated input data." }, { "source": "mlsys.convolution-lowering", "relation": "implements", "target": "mlsys.convolution", "statement": "implements", "conditions": "A lowering algorithm is chosen per layer shape." }, { "source": "mlsys.kernel-library", "relation": "reduces", "target": "mlsys.fusion", "statement": "can limit opportunities for", "conditions": "Library calls are opaque to the compiler and cannot be fused with neighbors." }, { "source": "mlsys.intermediate-representation", "relation": "enables", "target": "mlsys.fusion", "statement": "enables", "conditions": "The IR exposes producer-consumer structure across operators." }, { "source": "mlsys.autotuning", "relation": "determines", "target": "mlsys.tiling", "statement": "selects parameters for", "conditions": "The schedule space is large and performance is hard to predict analytically." }, { "source": "mlsys.kernel-dsl", "relation": "uses", "target": "mlsys.tiling", "statement": "is written in terms of", "conditions": "Kernels are written in terms of blocks that the compiler maps to threads and memory." }, { "source": "mlsys.memory-planning", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "reduces peak", "conditions": "Tensor lifetimes are known at compile time." }, { "source": "mlsys.device-streams", "relation": "enables", "target": "mlsys.communication-overlap", "statement": "enables", "conditions": "Communication and computation are issued on independent streams with correct event dependencies." }, { "source": "mlsys.graph-replay", "relation": "reduces", "target": "mlsys.dispatch", "statement": "reduces", "conditions": "The launch sequence and tensor addresses are static across iterations." }, { "source": "mlsys.loss-scaling", "relation": "part-of", "target": "mlsys.mixed-precision", "statement": "is a component of", "conditions": "Gradients would otherwise underflow in the reduced-precision format." }, { "source": "mlsys.block-scaled-format", "relation": "increases", "target": "mlsys.precision", "statement": "extends the dynamic range available at low", "conditions": "Values within each block have similar magnitude." }, { "source": "mlsys.accumulation-precision", "relation": "determines", "target": "mlsys.numerical-stability", "statement": "affects", "conditions": "Long reductions accumulate rounding error in low precision." }, { "source": "mlsys.expert-parallel", "relation": "requires", "target": "mlsys.all-to-all", "statement": "requires", "conditions": "Tokens are dispatched to and gathered from experts on other devices." }, { "source": "mlsys.expert-parallel", "relation": "enables", "target": "mlsys.mixture-of-experts", "statement": "distributes the experts of", "conditions": "Experts do not fit, or run too slowly, on one device." }, { "source": "mlsys.sequence-parallel", "relation": "increases", "target": "mlsys.context", "statement": "extends the feasible", "conditions": "Activation or attention memory for one sequence exceeds one device." }, { "source": "mlsys.network-topology", "relation": "determines", "target": "mlsys.hybrid-parallel", "statement": "shapes the group mapping of", "conditions": "Link bandwidth differs between levels of the hardware hierarchy." }, { "source": "mlsys.pipeline-parallel", "relation": "produces", "target": "mlsys.pipeline-bubble", "statement": "incurs", "conditions": "Microbatches per step are few relative to pipeline stages." }, { "source": "mlsys.ring-allreduce", "relation": "implements", "target": "mlsys.collectives", "statement": "is an algorithm for", "conditions": "Participants can be arranged in a ring with comparable link bandwidth." }, { "source": "mlsys.alpha-beta-model", "relation": "measures", "target": "mlsys.ring-allreduce", "statement": "predicts the cost of", "conditions": "Link latency and bandwidth are roughly uniform and contention is ignored." }, { "source": "mlsys.tree-allreduce", "relation": "alternative-to", "target": "mlsys.ring-allreduce", "statement": "is an alternative to", "conditions": "Messages are small or participants many, so latency terms dominate." }, { "source": "mlsys.bisection-bandwidth", "relation": "limits", "target": "mlsys.all-to-all", "statement": "bounds throughput of", "conditions": "Traffic crosses the network bisection rather than staying within local groups." }, { "source": "mlsys.data-parallel", "relation": "uses", "target": "mlsys.gradient-compression", "statement": "reduces communication volume of", "conditions": "Convergence is preserved, for example through error feedback, at the chosen compression ratio." }, { "source": "mlsys.lease", "relation": "requires", "target": "mlsys.fencing", "statement": "needs epoch fencing to be safe, as in", "conditions": "Clocks are bounded in drift so expired holders can be rejected safely." }, { "source": "mlsys.leader-election", "relation": "uses", "target": "mlsys.consensus", "statement": "commonly relies on", "conditions": "Replicas must agree on a single leader under partial failure." }, { "source": "mlsys.bounded-staleness", "relation": "mitigates", "target": "mlsys.stragglers", "statement": "reduces waiting on", "conditions": "Convergence tolerates updates computed on stale parameters within the bound." }, { "source": "mlsys.federated-averaging", "relation": "implements", "target": "mlsys.federated-learning", "statement": "is a baseline algorithm for", "conditions": "Clients can run local optimization steps between communication rounds." }, { "source": "mlsys.non-iid-data", "relation": "limits", "target": "mlsys.federated-averaging", "statement": "slows convergence of", "conditions": "Client data distributions differ and clients take many local steps." }, { "source": "mlsys.client-sampling", "relation": "increases", "target": "mlsys.non-iid-data", "statement": "can amplify bias from", "conditions": "Participation correlates with client characteristics such as connectivity or device class." }, { "source": "mlsys.federation-setting", "relation": "determines", "target": "mlsys.client-sampling", "statement": "determines constraints on", "conditions": "Device availability and reliability differ between cross-device and cross-silo settings." }, { "source": "mlsys.pue", "relation": "determines", "target": "mlsys.energy", "statement": "scales IT energy to facility energy in", "conditions": "The accounting boundary includes facility overheads." }, { "source": "mlsys.liquid-cooling", "relation": "enables", "target": "mlsys.rack-power-density", "statement": "raises the feasible", "conditions": "Air cooling cannot remove the heat generated at the target density." }, { "source": "mlsys.rack-power-density", "relation": "limits", "target": "mlsys.scale-up-interconnect", "statement": "constrains the physical extent of", "conditions": "Short copper links require accelerators to be packed into few racks." }, { "source": "mlsys.power-oversubscription", "relation": "requires", "target": "mlsys.power-capping", "statement": "relies on", "conditions": "Simultaneous peaks across equipment are rare but must be bounded when they occur." }, { "source": "mlsys.rdma", "relation": "reduces", "target": "mlsys.collectives", "statement": "lowers per-message overhead of", "conditions": "Transfers bypass host network stacks and remote CPUs." }, { "source": "mlsys.lossless-fabric", "relation": "enables", "target": "mlsys.rdma", "statement": "provides drop-free transport for", "conditions": "The RDMA transport performs poorly under packet loss, as with RoCE." }, { "source": "mlsys.congestion-control", "relation": "mitigates", "target": "mlsys.stragglers", "statement": "can reduce network-induced", "conditions": "Synchronized collective traffic or incast creates switch queue buildup." }, { "source": "mlsys.fat-tree", "relation": "increases", "target": "mlsys.bisection-bandwidth", "statement": "can provide full", "conditions": "Switch tiers are not oversubscribed." }, { "source": "mlsys.rail-optimized", "relation": "enables", "target": "mlsys.hybrid-parallel", "statement": "keeps collective traffic within one switch hop for", "conditions": "Parallel groups are placed so peers share the same rail." }, { "source": "mlsys.in-network-reduction", "relation": "implements", "target": "mlsys.tree-allreduce", "statement": "offloads reduction steps of", "conditions": "Switches support aggregation for the data types and operations used." }, { "source": "mlsys.bisection-bandwidth", "relation": "measures", "target": "mlsys.network-topology", "statement": "summarizes the capacity of", "conditions": "Traffic is uniform enough that the worst-case cut is relevant." }, { "source": "mlsys.parallel-file-system", "relation": "enables", "target": "mlsys.input-pipeline", "statement": "supplies aggregate read bandwidth for", "conditions": "Many workers read training data concurrently from shared storage." }, { "source": "mlsys.object-storage", "relation": "part-of", "target": "mlsys.storage-tiering", "statement": "commonly forms the capacity tier in", "conditions": "Hot data is cached closer to compute." }, { "source": "mlsys.input-pipeline", "relation": "uses", "target": "mlsys.storage-tiering", "statement": "reads through the nearer tiers of", "conditions": "Working sets fit in nearer tiers or are prefetched." }, { "source": "mlsys.checkpoint-io", "relation": "determines", "target": "mlsys.checkpoint", "statement": "sets the cost of taking", "conditions": "Training state is large relative to available storage bandwidth." }, { "source": "mlsys.gang-scheduling", "relation": "reduces", "target": "mlsys.utilization", "statement": "can reduce cluster", "conditions": "Resources sit idle while waiting for a full allocation, unless backfilled." }, { "source": "mlsys.job-preemption", "relation": "requires", "target": "mlsys.checkpoint", "statement": "depends on", "conditions": "Preempted work is resumed rather than restarted from scratch." }, { "source": "mlsys.dominant-resource-fairness", "relation": "implements", "target": "mlsys.multi-tenancy", "statement": "allocates shares among tenants in", "conditions": "Jobs demand several resource types in different proportions." }, { "source": "mlsys.elastic-training", "relation": "mitigates", "target": "mlsys.job-preemption", "statement": "reduces lost work from", "conditions": "The job can reshard state and continue with fewer workers." }, { "source": "mlsys.topology-aware-placement", "relation": "reduces", "target": "mlsys.collectives", "statement": "reduces the cost of", "conditions": "Communicating workers are placed on shared fast links or switches." }, { "source": "mlsys.topology-aware-placement", "relation": "specializes", "target": "mlsys.placement", "statement": "refines", "conditions": "Placement decisions are made at node and device granularity." }, { "source": "mlsys.bin-packing", "relation": "trades-off", "target": "mlsys.topology-aware-placement", "statement": "trades off against", "conditions": "Dense packing fragments the contiguous allocations that locality requires." }, { "source": "mlsys.multi-tenancy", "relation": "requires", "target": "mlsys.isolation", "statement": "requires", "conditions": "Tenants must not observe or disrupt one another's work." }, { "source": "mlsys.resource-quota", "relation": "part-of", "target": "mlsys.capacity-planning", "statement": "partitions shared capacity within", "conditions": "Shared capacity is divided among tenants with stated limits." }, { "source": "mlsys.gpu-partitioning", "relation": "increases", "target": "mlsys.utilization", "statement": "can raise", "conditions": "Individual workloads cannot saturate a full accelerator." }, { "source": "mlsys.performance-interference", "relation": "increases", "target": "mlsys.tail-latency", "statement": "can inflate", "conditions": "Co-located workloads contend for unpartitioned shared resources." }, { "source": "mlsys.spot-capacity", "relation": "causes", "target": "mlsys.job-preemption", "statement": "exposes workloads to", "conditions": "The provider reclaims capacity on short notice." }, { "source": "mlsys.spot-capacity", "relation": "reduces", "target": "mlsys.unit-cost", "statement": "can lower", "conditions": "Interruption overhead is smaller than the price discount." }, { "source": "mlsys.weak-supervision", "relation": "determines", "target": "mlsys.labels", "statement": "produces higher-volume, noisier", "conditions": "Holds when labeling sources are noisy and their errors must be modeled or denoised before training." }, { "source": "mlsys.active-learning", "relation": "determines", "target": "mlsys.data-acquisition", "statement": "biases sampling in", "conditions": "The selected examples depend on the current model, so the labeled set is not a random sample of the deployment population." }, { "source": "mlsys.annotator-agreement", "relation": "measures", "target": "mlsys.labels", "statement": "measures reliability of", "conditions": "Holds when multiple independent annotations of the same items are collected." }, { "source": "mlsys.quality-filtering", "relation": "implements", "target": "mlsys.data-selection", "statement": "implements", "conditions": "The quality score must correlate with downstream value; filtering on a narrow proxy can remove useful diversity." }, { "source": "mlsys.data-mixture", "relation": "determines", "target": "mlsys.generalization", "statement": "shifts across domains", "conditions": "Holds under a fixed training budget, where raising one domain's share reduces exposure to others." }, { "source": "mlsys.synthetic-data", "relation": "causes", "target": "mlsys.distribution-shift", "statement": "can introduce", "conditions": "Holds when the generator's output distribution differs from the deployment distribution or repeats the generator's errors." }, { "source": "mlsys.data-augmentation", "relation": "increases", "target": "mlsys.generalization", "statement": "can improve", "conditions": "The transformations must preserve labels and reflect invariances present at deployment." }, { "source": "mlsys.data-validation", "relation": "implements", "target": "mlsys.data-contract", "statement": "enforces", "conditions": "The contract's schema and validity rules must be machine-checkable." }, { "source": "mlsys.feature-store", "relation": "mitigates", "target": "mlsys.train-serve-skew", "statement": "reduces", "conditions": "Holds when training and serving read features from the same definitions; it does not prevent skew from logic outside the store." }, { "source": "mlsys.feature-store", "relation": "enables", "target": "mlsys.point-in-time", "statement": "supports point-in-time joins for", "conditions": "Requires timestamped feature values and as-of joins when building training sets." }, { "source": "mlsys.stream-processing", "relation": "limits", "target": "mlsys.feature-freshness", "statement": "bounds achievable", "conditions": "Processing latency and windowing determine how recent a served feature value can be." }, { "source": "mlsys.feature-backfill", "relation": "requires", "target": "mlsys.point-in-time", "statement": "must preserve", "conditions": "Backfilled values must be computed only from data available at each historical timestamp." }, { "source": "mlsys.experiment-reproducibility", "relation": "requires", "target": "mlsys.data-versioning", "statement": "requires", "conditions": "Holds whenever training or evaluation data can change after a run." }, { "source": "mlsys.data-dependency", "relation": "increases", "target": "mlsys.entanglement", "statement": "amplifies", "conditions": "Holds when unstable or unused inputs remain in a model whose behavior depends jointly on all inputs." }, { "source": "mlsys.feature-freshness", "relation": "causes", "target": "mlsys.train-serve-skew", "statement": "stale values can cause", "conditions": "Holds when serving-time feature age differs from the age assumed when training examples were built." }, { "source": "mlsys.learning-rate-schedule", "relation": "mitigates", "target": "mlsys.training-instability", "statement": "mitigates", "conditions": "Warmup and decay reduce early and late divergence for many optimizers but do not remove instabilities from data or numerics." }, { "source": "mlsys.critical-batch-size", "relation": "limits", "target": "mlsys.data-parallel", "statement": "limits useful scaling of", "conditions": "Holds when adding replicas increases the global batch beyond the critical batch size." }, { "source": "mlsys.checkpoint", "relation": "mitigates", "target": "mlsys.training-instability", "statement": "enables restarts that recover from", "conditions": "Holds when loss spikes are handled by rolling back to an earlier checkpoint and changing data order or hyperparameters." }, { "source": "mlsys.gradient-accumulation", "relation": "enables", "target": "mlsys.batch-size", "statement": "enables large effective batch sizes within limited memory for", "conditions": "Holds for losses that sum over examples; per-batch statistics such as batch normalization still see micro-batches." }, { "source": "mlsys.memory-offloading", "relation": "reduces", "target": "mlsys.training-state", "statement": "enables state larger than accelerator memory for", "conditions": "Benefit depends on host-device bandwidth and how well transfers overlap with compute." }, { "source": "mlsys.experiment-tracking", "relation": "enables", "target": "mlsys.experiment-reproducibility", "statement": "records evidence for", "conditions": "Tracking must capture code, data, configuration, and environment versions, not only metrics." }, { "source": "mlsys.hyperparameter-transfer", "relation": "reduces", "target": "mlsys.experiment-budget", "statement": "reduces large-scale spend of", "conditions": "Holds only under a parameterization in which optimal hyperparameters remain stable as width or depth grows." }, { "source": "mlsys.lora", "relation": "specializes", "target": "mlsys.peft", "statement": "is an instance of", "conditions": "Always; LoRA restricts updates to low-rank added matrices." }, { "source": "mlsys.supervised-fine-tuning", "relation": "causes", "target": "mlsys.catastrophic-forgetting", "statement": "can cause", "conditions": "Holds when fine-tuning data are narrow relative to pretraining and no replay or regularization is used." }, { "source": "mlsys.reward-model", "relation": "part-of", "target": "mlsys.rlhf", "statement": "supplies the training signal in", "conditions": "Holds for RLHF pipelines that learn a reward model from human comparisons." }, { "source": "mlsys.rlhf", "relation": "causes", "target": "mlsys.reward-hacking", "statement": "is exposed to", "conditions": "Holds when the policy optimizes a learned reward beyond the region where it tracks human preference; a reference-model penalty reduces but does not remove it." }, { "source": "mlsys.rlvr", "relation": "requires", "target": "mlsys.verifier", "statement": "requires", "conditions": "Rewards are only as reliable as the programmatic checker; exploitable checkers produce reward hacking." }, { "source": "mlsys.slo-goodput", "relation": "limits", "target": "mlsys.rl-rollout", "statement": "bounds the throughput of", "conditions": "Holds when rollout generation, not policy update, dominates step time, so serving optimizations raise training throughput." }, { "source": "mlsys.weight-sync", "relation": "determines", "target": "mlsys.policy-staleness", "statement": "frequency determines", "conditions": "Holds when rollout workers continue generating between synchronizations." }, { "source": "mlsys.policy-staleness", "relation": "causes", "target": "mlsys.training-instability", "statement": "can induce", "conditions": "Holds when the update rule assumes on-policy data and lag is not corrected by importance weighting or clipping." }, { "source": "mlsys.online-learning", "relation": "determines", "target": "mlsys.feedback-loop", "statement": "shortens the cycle of", "conditions": "Holds when the model's own predictions influence the data it next learns from." }, { "source": "mlsys.incremental-training", "relation": "specializes", "target": "mlsys.retraining", "statement": "is a warm-start, lower-cost kind of", "conditions": "Holds when the model architecture and feature set are unchanged; schema or architecture changes require training from scratch." }, { "source": "mlsys.experience-replay", "relation": "mitigates", "target": "mlsys.catastrophic-forgetting", "statement": "mitigates", "conditions": "Effectiveness depends on buffer size and how representative stored examples are of earlier tasks." }, { "source": "mlsys.memory-capacity", "relation": "limits", "target": "mlsys.on-device-training", "statement": "constrains", "conditions": "Holds on devices whose memory cannot hold full activations and optimizer state, forcing sparse or partial updates." }, { "source": "mlsys.cross-validation", "relation": "reduces", "target": "mlsys.statistical-uncertainty", "statement": "reduces variance of estimates under", "conditions": "Holds for i.i.d. data; temporal or grouped data need splits that respect dependence." }, { "source": "mlsys.sequential-testing", "relation": "enables", "target": "mlsys.online-experiment", "statement": "enables valid early stopping of", "conditions": "Holds when the analysis uses a sequential procedure; repeated peeking at fixed-horizon tests inflates false positives." }, { "source": "mlsys.experiment-interference", "relation": "limits", "target": "mlsys.online-experiment", "statement": "can bias estimates from", "conditions": "Holds when units share resources, markets, or networks, so treatment of one unit affects others." }, { "source": "mlsys.conformal-prediction", "relation": "enables", "target": "mlsys.selective-prediction", "statement": "provides coverage-calibrated sets for", "conditions": "Coverage holds marginally under exchangeability between calibration and test data, not under distribution shift." }, { "source": "mlsys.slice-discovery", "relation": "enables", "target": "mlsys.sliced-evaluation", "statement": "proposes slices for", "conditions": "Discovered slices require human review for coherence and multiple-comparison effects." }, { "source": "mlsys.llm-judge", "relation": "requires", "target": "mlsys.human-evaluation", "statement": "must be calibrated against", "conditions": "Holds whenever model-graded scores are used as evidence; judges show position, length, and self-preference biases." }, { "source": "mlsys.pass-at-k", "relation": "limits", "target": "mlsys.best-of-n", "statement": "upper-bounds accuracy of", "conditions": "Best-of-n reaches pass@n only with a perfect selector; real verifiers lose part of the gap." }, { "source": "mlsys.statistical-uncertainty", "relation": "limits", "target": "mlsys.pairwise-preference-eval", "statement": "requires reporting of", "conditions": "Holds because rankings from finite pairwise votes carry sampling error that can reorder close models." }, { "source": "mlsys.benchmark-contamination", "relation": "specializes", "target": "mlsys.leakage", "statement": "is a form of", "conditions": "Holds when evaluation items or near-duplicates appear in training corpora; detection needs overlap checks against training data." }, { "source": "mlsys.deduplication", "relation": "mitigates", "target": "mlsys.benchmark-contamination", "statement": "reduces", "conditions": "Holds when deduplication is run against evaluation sets, not only within the training corpus." }, { "source": "mlsys.time-to-train", "relation": "part-of", "target": "mlsys.benchmark", "statement": "is the headline metric of a training", "conditions": "Holds for training benchmarks that fix model, dataset, and quality target under run rules." }, { "source": "mlsys.inference-load-scenario", "relation": "limits", "target": "mlsys.tail-latency", "statement": "sets the latency-percentile constraint on", "conditions": "Holds for server-style scenarios that report throughput only at a stated latency percentile." }, { "source": "mlsys.metamorphic-testing", "relation": "implements", "target": "mlsys.test-oracle", "statement": "substitutes output relations for a missing", "conditions": "Holds when a valid metamorphic relation between inputs and outputs is known." }, { "source": "mlsys.production-readiness", "relation": "determines", "target": "mlsys.release-gate", "statement": "supplies criteria for", "conditions": "Holds when rubric items are converted into automated or reviewed checks." }, { "source": "mlsys.batch-inference", "relation": "reduces", "target": "mlsys.feature-freshness", "statement": "increases the staleness (age at use) of", "conditions": "Holds when precomputed predictions are served after their inputs have changed." }, { "source": "mlsys.tail-latency", "relation": "measures", "target": "mlsys.online-inference", "statement": "is the judging metric of", "conditions": "Holds for user-facing request paths with latency targets." }, { "source": "mlsys.model-server", "relation": "uses", "target": "mlsys.batching", "statement": "hosts", "conditions": "Holds for servers that queue concurrent requests to batch them on shared accelerators." }, { "source": "mlsys.multi-model-serving", "relation": "increases", "target": "mlsys.cold-start", "statement": "increases exposure to load-time", "conditions": "Holds when more models than fit in memory share resources, so infrequently used models must be loaded on demand." }, { "source": "mlsys.model-cascade", "relation": "reduces", "target": "mlsys.unit-cost", "statement": "can reduce", "conditions": "Holds when most requests are resolved by cheap models and the escalation criterion is reliable." }, { "source": "mlsys.model-cascade", "relation": "requires", "target": "mlsys.selective-prediction", "statement": "relies on", "conditions": "Each stage must abstain and defer when confidence is low; poorly calibrated scores misroute requests." }, { "source": "mlsys.slo", "relation": "determines", "target": "mlsys.latency-budget", "statement": "sets the total allocated by", "conditions": "Holds when the end-to-end SLO is decomposed into per-stage targets that sum within it." }, { "source": "mlsys.prefill-decode", "relation": "determines", "target": "mlsys.ttft", "statement": "prefill phase dominates", "conditions": "Holds when queueing delay is small relative to prompt processing time." }, { "source": "mlsys.memory-bandwidth", "relation": "limits", "target": "mlsys.tpot", "statement": "bounds", "conditions": "Holds in decode at small batch, where each step reads weights and KV cache with low arithmetic intensity." }, { "source": "mlsys.hedged-requests", "relation": "reduces", "target": "mlsys.tail-latency", "statement": "reduces", "conditions": "Holds when slow responses come from replica-specific variability and the extra load from duplicates is small." }, { "source": "mlsys.continuous-batching", "relation": "specializes", "target": "mlsys.batching", "statement": "specializes", "conditions": "Applies to iterative generation, where requests finish at different steps." }, { "source": "mlsys.continuous-batching", "relation": "increases", "target": "mlsys.slo-goodput", "statement": "increases", "conditions": "Holds when KV memory admits enough concurrent sequences; interference between prefill and decode can raise latency." }, { "source": "mlsys.load-balancing", "relation": "determines", "target": "mlsys.prefix-cache", "statement": "determines hit rate of", "conditions": "Holds when cached prefixes are replica-local, so cache-aware routing raises reuse but can unbalance load." }, { "source": "mlsys.cold-start", "relation": "limits", "target": "mlsys.autoscaling", "statement": "bounds how quickly it can react:", "conditions": "Holds when new replicas must load large weights before serving, so scaling must anticipate demand." }, { "source": "mlsys.chunked-prefill", "relation": "reduces", "target": "mlsys.tpot", "statement": "reduces decode stalls in", "conditions": "Holds when long prefills would otherwise stall decode iterations of co-batched requests; it can raise TTFT for the long prompt." }, { "source": "mlsys.pd-disaggregation", "relation": "determines", "target": "mlsys.prefill-decode", "statement": "provisions separate resources for each phase of", "conditions": "Pays off when interference between phases costs more than transferring KV state between pools." }, { "source": "mlsys.pd-disaggregation", "relation": "increases", "target": "mlsys.slo-goodput", "statement": "can increase", "conditions": "Holds when TTFT and TPOT targets would otherwise force over-provisioning of a colocated deployment." }, { "source": "mlsys.constrained-decoding", "relation": "implements", "target": "mlsys.grammar", "statement": "enforces", "conditions": "Holds when the constraint compiles to an automaton aligned with the tokenizer's vocabulary." }, { "source": "mlsys.output-validation", "relation": "alternative-to", "target": "mlsys.constrained-decoding", "statement": "is the post-hoc alternative to", "conditions": "Validation catches semantic rule violations decoding cannot express, at the cost of retries." }, { "source": "mlsys.self-consistency", "relation": "requires", "target": "mlsys.sampling-controls", "statement": "requires sampling diversity from", "conditions": "Voting gains require sampled paths to differ; greedy decoding yields identical candidates." }, { "source": "mlsys.reasoning-budget", "relation": "increases", "target": "mlsys.tail-latency", "statement": "larger budgets increase", "conditions": "Holds because generated reasoning length varies widely across requests and adds decode steps." }, { "source": "mlsys.verifier", "relation": "limits", "target": "mlsys.best-of-n", "statement": "precision bounds the gains of", "conditions": "Holds as n grows, since selection pressure increasingly exploits verifier errors." }, { "source": "mlsys.process-reward-model", "relation": "specializes", "target": "mlsys.verifier", "statement": "is a step-level form of", "conditions": "Requires step-level labels or estimates of step correctness." }, { "source": "mlsys.inference-time-search", "relation": "uses", "target": "mlsys.process-reward-model", "statement": "uses for pruning", "conditions": "Holds for searches that score partial solutions before completion." }, { "source": "mlsys.test-time-scaling", "relation": "specializes", "target": "mlsys.scaling-laws", "statement": "extends to inference compute", "conditions": "Holds within task families where extra samples or reasoning steps improve accuracy; gains saturate and vary by difficulty." }, { "source": "mlsys.test-time-scaling", "relation": "determines", "target": "mlsys.unit-cost", "statement": "sets the quality gained per unit of", "conditions": "Holds whenever additional per-query compute is billed or consumes shared serving capacity." }, { "source": "mlsys.rag", "relation": "mitigates", "target": "mlsys.hallucination", "statement": "can reduce", "conditions": "Holds when retrieved passages are relevant and the model grounds its claims in them; irrelevant retrievals can mislead." }, { "source": "mlsys.rag", "relation": "requires", "target": "mlsys.retrieval", "statement": "depends on", "conditions": "Generation quality is bounded by retrieval recall for the facts the answer needs." }, { "source": "mlsys.dense-retrieval", "relation": "uses", "target": "mlsys.ann", "statement": "uses at scale", "conditions": "Holds when the collection is too large for exact similarity search within latency limits." }, { "source": "mlsys.lexical-retrieval", "relation": "alternative-to", "target": "mlsys.dense-retrieval", "statement": "is a complementary alternative to", "conditions": "Lexical matching handles rare terms and exact identifiers that embeddings may miss; hybrid systems combine both scores." }, { "source": "mlsys.reranking", "relation": "part-of", "target": "mlsys.retrieval", "statement": "is the precision stage of", "conditions": "Holds when first-stage recall is adequate and reranker cost fits the latency budget." }, { "source": "mlsys.model-conversion", "relation": "uses", "target": "mlsys.quantization", "statement": "often applies", "conditions": "Holds for integer-only targets; converted models must be re-evaluated because numerics change." }, { "source": "mlsys.memory-capacity", "relation": "limits", "target": "mlsys.on-device-runtime", "statement": "constrains", "conditions": "Holds on microcontrollers and mobile devices where weights, activations, and runtime must fit in limited RAM and flash." }, { "source": "mlsys.ota-model-update", "relation": "uses", "target": "mlsys.rollout", "statement": "applies staged exposure from", "conditions": "Holds for device fleets where updates are staged by cohort and devices may be offline." }, { "source": "mlsys.device-cloud-partitioning", "relation": "specializes", "target": "mlsys.placement", "statement": "is a form of", "conditions": "Holds when part of the computation can run on either side of a network link." }, { "source": "mlsys.device-heterogeneity", "relation": "increases", "target": "mlsys.model-conversion", "statement": "multiplies targets and effort for", "conditions": "Holds when devices differ in supported operators, precision, or accelerators." }, { "source": "mlsys.model-registry", "relation": "implements", "target": "mlsys.artifact-version", "statement": "stores and governs", "conditions": "Holds when every deployable model is registered with its lineage and stage." }, { "source": "mlsys.shadow-deployment", "relation": "part-of", "target": "mlsys.rollout", "statement": "is the zero-exposure stage before", "conditions": "Holds when candidate outputs can be compared without user exposure; shadowing cannot measure effects of users reacting to the new model." }, { "source": "mlsys.ml-ci-cd", "relation": "implements", "target": "mlsys.release-gate", "statement": "automates", "conditions": "Holds when gate criteria are machine-checkable; some require human review." }, { "source": "mlsys.release-gate", "relation": "determines", "target": "mlsys.rollout", "statement": "controls advancement of", "conditions": "Holds when each rollout stage has predeclared pass criteria." }, { "source": "mlsys.drift-detection", "relation": "detects", "target": "mlsys.distribution-shift", "statement": "detects", "conditions": "Holds for shifts visible in monitored statistics; detected drift need not degrade quality, and some harmful shifts are not detected." }, { "source": "mlsys.inference-logging", "relation": "causes", "target": "mlsys.privacy-leakage", "statement": "can increase exposure to", "conditions": "Holds when logs retain personal inputs or outputs without redaction, access control, or retention limits." }, { "source": "mlsys.alerting", "relation": "part-of", "target": "mlsys.incident-response", "statement": "initiates", "conditions": "Holds when alerts are actionable; excessive alerting degrades response through fatigue." }, { "source": "mlsys.retraining", "relation": "mitigates", "target": "mlsys.concept-drift", "statement": "mitigates, given fresh labels,", "conditions": "Holds because input monitoring alone cannot reveal changed input-target relations; updated outcomes are needed." }, { "source": "mlsys.continuous-training", "relation": "implements", "target": "mlsys.retraining", "statement": "automates", "conditions": "Holds when data validation and release gates are automated so retrained models can be promoted without manual steps." }, { "source": "mlsys.postmortem", "relation": "part-of", "target": "mlsys.incident-response", "statement": "closes the loop on", "conditions": "Holds when corrective actions are tracked to completion." }, { "source": "mlsys.failure-attribution", "relation": "requires", "target": "mlsys.observability", "statement": "depends on", "conditions": "Distinguishing data, model, and infrastructure causes requires versioned, correlated signals from each layer." }, { "source": "mlsys.configuration-management", "relation": "reduces", "target": "mlsys.technical-debt", "statement": "reduces configuration", "conditions": "Holds when configuration is reviewed, tested, and versioned like code." }, { "source": "mlsys.infrastructure-as-code", "relation": "determines", "target": "mlsys.dependency-environment", "statement": "declares and reproduces", "conditions": "Holds when declarations pin versions and are applied without manual drift." }, { "source": "mlsys.ml-platform", "relation": "reduces", "target": "mlsys.technical-debt", "statement": "can amortize", "conditions": "Holds when many teams adopt shared components; a poorly maintained platform becomes debt itself." }, { "source": "mlsys.undeclared-consumers", "relation": "causes", "target": "mlsys.feedback-loop", "statement": "can create hidden", "conditions": "Holds when unknown consumers act on model outputs in ways that change future training data." }, { "source": "mlsys.entanglement", "relation": "increases", "target": "mlsys.technical-debt", "statement": "is a source of", "conditions": "Holds for models where inputs interact, so isolated changes have non-local effects." }, { "source": "mlsys.continuous-training", "relation": "part-of", "target": "mlsys.mlops-maturity", "statement": "is a defining capability of higher levels of", "conditions": "Holds for maturity models that define levels by degree of pipeline automation." }, { "source": "mlsys.ml-roles", "relation": "determines", "target": "mlsys.ml-platform", "statement": "assigns ownership of", "conditions": "Holds in organizations large enough to separate platform engineering from model development." }, { "source": "mlsys.undeclared-consumers", "relation": "limits", "target": "mlsys.model-deprecation", "statement": "can block or break", "conditions": "Holds when consumers cannot be identified and migrated before the model is removed." }, { "source": "mlsys.artifact-archival", "relation": "enables", "target": "mlsys.experiment-reproducibility", "statement": "preserves", "conditions": "Holds when archives include data references, environments, and configuration as well as weights." }, { "source": "mlsys.data-retention", "relation": "implements", "target": "mlsys.privacy", "statement": "implements requirements of", "conditions": "Holds when retention limits apply to raw data, logs, and derived artifacts such as features and models." }, { "source": "mlsys.data-retention", "relation": "trades-off", "target": "mlsys.artifact-archival", "statement": "trades off against", "conditions": "Holds when audit or reproduction needs conflict with deletion obligations." }, { "source": "mlsys.model-flops-utilization", "relation": "specializes", "target": "mlsys.utilization", "statement": "specializes", "conditions": "Holds when utilization is defined as model-required FLOPs per second over aggregate peak FLOP rate, excluding recomputed work." }, { "source": "mlsys.roofline", "relation": "limits", "target": "mlsys.model-flops-utilization", "statement": "bounds attainable", "conditions": "For a given kernel mix, attainable MFU cannot exceed the roofline bound implied by each kernel's arithmetic intensity." }, { "source": "mlsys.profiling", "relation": "enables", "target": "mlsys.bottleneck-analysis", "statement": "supplies evidence for", "conditions": "When the profile covers the same workload, configuration, and execution boundary as the bottleneck claim." }, { "source": "mlsys.bottleneck-analysis", "relation": "uses", "target": "mlsys.roofline", "statement": "uses", "conditions": "When the candidate bottleneck is a kernel's compute or memory-bandwidth bound rather than I/O, scheduling, or communication." }, { "source": "mlsys.pruning", "relation": "produces", "target": "mlsys.sparsity", "statement": "produces", "conditions": "When pruning removes individual weights, leaving an irregular zero pattern whose speedup depends on sparse-execution support." }, { "source": "mlsys.structured-pruning", "relation": "specializes", "target": "mlsys.pruning", "statement": "specializes", "conditions": "When the pruned unit is a whole channel, filter, head, or layer rather than an individual weight." }, { "source": "mlsys.structured-pruning", "relation": "alternative-to", "target": "mlsys.sparsity", "statement": "is a dense-hardware-friendly alternative to irregular", "conditions": "Coarse granularity yields speedups on dense hardware but usually loses more quality than unstructured sparsity at equal parameter reduction." }, { "source": "mlsys.low-rank-factorization", "relation": "reduces", "target": "mlsys.matrix-multiply", "statement": "reduces the cost of", "conditions": "When weight matrices are well approximated at a rank much smaller than their dimensions and the smaller products still run efficiently." }, { "source": "mlsys.quantization-aware-training", "relation": "specializes", "target": "mlsys.quantization", "statement": "mitigates quality loss from", "conditions": "When the training-time quantization simulation matches the deployed encoding and rounding rules." }, { "source": "mlsys.neural-architecture-search", "relation": "specializes", "target": "mlsys.hyperparameter-search", "statement": "is a structured form of", "conditions": "When architectural choices are treated as a structured search space evaluated under an experiment budget." }, { "source": "mlsys.hardware-aware-nas", "relation": "specializes", "target": "mlsys.neural-architecture-search", "statement": "specializes", "conditions": "When the search objective includes a cost measured or predicted on the target platform." }, { "source": "mlsys.hardware-aware-nas", "relation": "reduces", "target": "mlsys.execution-time", "statement": "optimizes measured", "conditions": "When latency on the target device is measured or predicted directly, since FLOP counts can rank architectures differently from real latency." }, { "source": "mlsys.depthwise-separable-convolution", "relation": "reduces", "target": "mlsys.arithmetic-intensity", "statement": "lowers", "conditions": "Fewer operations per output come with less data reuse, so memory-bound depthwise layers may not speed up in proportion to the FLOP reduction." }, { "source": "mlsys.efficiency-frontier", "relation": "requires", "target": "mlsys.metrics", "statement": "is defined only for stated", "conditions": "A frontier is defined only for a stated quality metric, cost measure, and target platform." }, { "source": "mlsys.model-hardware-co-design", "relation": "determines", "target": "mlsys.efficiency-frontier", "statement": "can shift outward", "conditions": "When model structure and hardware or kernels are designed together rather than one being fixed while the other is tuned." }, { "source": "mlsys.model-hardware-co-design", "relation": "determines", "target": "mlsys.accelerator-execution-architecture", "statement": "jointly specifies", "conditions": "When the accelerator's dataflow, precision support, or memory hierarchy is a design variable alongside the model." }, { "source": "mlsys.retry-backoff", "relation": "requires", "target": "mlsys.idempotency", "statement": "requires", "conditions": "Retrying an operation with external effects is safe only when the operation is idempotent or deduplicated." }, { "source": "mlsys.circuit-breaker", "relation": "limits", "target": "mlsys.retry-backoff", "statement": "bounds", "conditions": "When the breaker's error threshold trips before retries saturate the failing dependency." }, { "source": "mlsys.replication-failover", "relation": "increases", "target": "mlsys.availability", "statement": "raises", "conditions": "When replica failures are independent and failover completes faster than the outage it masks." }, { "source": "mlsys.failure-domain", "relation": "limits", "target": "mlsys.replication-failover", "statement": "limits the protection of", "conditions": "When replicas share a failure domain, a single cause can take out all of them together." }, { "source": "mlsys.mtbf", "relation": "determines", "target": "mlsys.checkpoint-interval", "statement": "determines", "conditions": "Under approximately exponential failure times, where the optimal interval scales with the square root of checkpoint cost times MTBF." }, { "source": "mlsys.mtbf", "relation": "increases", "target": "mlsys.availability", "statement": "raises", "conditions": "For a repairable component in steady state, availability is approximately MTBF divided by MTBF plus mean time to repair." }, { "source": "mlsys.checkpoint-interval", "relation": "determines", "target": "mlsys.training-goodput", "statement": "maximizes, when well chosen,", "conditions": "Under a stated MTBF and checkpoint write cost, the interval trades checkpoint overhead against expected lost work." }, { "source": "mlsys.async-checkpointing", "relation": "reduces", "target": "mlsys.checkpoint-interval", "statement": "shortens", "conditions": "When the blocking snapshot to host memory costs much less than the durable write, lowering the effective checkpoint cost." }, { "source": "mlsys.elastic-training", "relation": "uses", "target": "mlsys.checkpoint", "statement": "resumes from", "conditions": "When the job restarts on a changed worker set and state must be resharded from the latest consistent checkpoint." }, { "source": "mlsys.elastic-training", "relation": "increases", "target": "mlsys.training-goodput", "statement": "can raise", "conditions": "When replacing failed nodes takes longer than continuing at reduced width and the optimizer tolerates the batch-size change." }, { "source": "mlsys.abft", "relation": "detects", "target": "mlsys.silent-data-corruption", "statement": "detects", "conditions": "For linear-algebra operations whose input checksums predict output checksums, within the floating-point tolerance used for comparison." }, { "source": "mlsys.gray-failure", "relation": "causes", "target": "mlsys.stragglers", "statement": "can manifest as", "conditions": "In synchronous distributed jobs, where one degraded but live node slows every step." }, { "source": "mlsys.chaos-engineering", "relation": "uses", "target": "mlsys.failure-injection", "statement": "uses", "conditions": "When injected faults are framed as experiments against a stated steady-state hypothesis." }, { "source": "mlsys.blast-radius", "relation": "limits", "target": "mlsys.chaos-engineering", "statement": "bounds", "conditions": "When experiments run against production traffic and must limit the users and components they can affect." }, { "source": "mlsys.failure-domain", "relation": "limits", "target": "mlsys.blast-radius", "statement": "bounds", "conditions": "When isolation between failure domains holds, so a fault does not propagate across shared dependencies." }, { "source": "mlsys.game-day", "relation": "measures", "target": "mlsys.incident-response", "statement": "rehearses", "conditions": "When the exercise uses the same detection, paging, and runbook paths as real incidents." }, { "source": "mlsys.hardware-fault-injection", "relation": "measures", "target": "mlsys.silent-data-corruption", "statement": "estimates model sensitivity to", "conditions": "When the injected fault model, location, and rate approximate the hardware's actual failure modes." }, { "source": "mlsys.backdoor", "relation": "specializes", "target": "mlsys.poisoning", "statement": "is a targeted form of", "conditions": "When poisoned training samples pair a trigger pattern with an attacker-chosen output while clean accuracy is preserved." }, { "source": "mlsys.supply-chain-compromise", "relation": "causes", "target": "mlsys.backdoor", "statement": "can implant a", "conditions": "When pretrained weights or fine-tuning data come from parties outside the defender's trust boundary." }, { "source": "mlsys.jailbreak", "relation": "specializes", "target": "mlsys.evasion", "statement": "specializes", "conditions": "When the adversarial input is a user prompt aimed at bypassing safety training rather than at a task prediction; it differs from prompt injection, where untrusted third-party content crosses an authority boundary." }, { "source": "mlsys.adversarial-training", "relation": "mitigates", "target": "mlsys.evasion", "statement": "reduces success of", "conditions": "Only within the perturbation set and threat model used to generate training adversaries." }, { "source": "mlsys.adversarial-training", "relation": "trades-off", "target": "mlsys.generalization", "statement": "trades off against clean", "conditions": "Robust training typically costs clean accuracy and multiplies training compute by the cost of generating adversaries." }, { "source": "mlsys.model-extraction", "relation": "produces", "target": "mlsys.model-artifact", "statement": "produces a functional copy of", "conditions": "When the attacker has sufficient query access and output detail, such as confidence scores, relative to model complexity." }, { "source": "mlsys.model-extraction", "relation": "enables", "target": "mlsys.evasion", "statement": "enables transfer attacks for", "conditions": "When adversarial inputs crafted against the extracted surrogate transfer to the deployed model." }, { "source": "mlsys.model-watermarking", "relation": "detects", "target": "mlsys.model-extraction", "statement": "provides ownership evidence against", "conditions": "When the watermark survives the adversary's fine-tuning, pruning, or distillation of the copied model." }, { "source": "mlsys.artifact-signing", "relation": "measures", "target": "mlsys.model-artifact", "statement": "verifies integrity of", "conditions": "When consumers check signatures against trusted identities before loading and the signing key is not compromised." }, { "source": "mlsys.artifact-signing", "relation": "uses", "target": "mlsys.lineage", "statement": "binds provenance from", "conditions": "When the signature covers provenance metadata such as training data and build inputs, not only the weight bytes." }, { "source": "mlsys.unsafe-deserialization", "relation": "enables", "target": "mlsys.supply-chain-compromise", "statement": "enables", "conditions": "When model files in executable serialization formats are loaded from untrusted sources without a data-only format such as safetensors." }, { "source": "mlsys.dependency-environment", "relation": "causes", "target": "mlsys.supply-chain-compromise", "statement": "propagates into", "conditions": "When dependencies are resolved from mutable or unverified sources at build or load time." }, { "source": "mlsys.membership-inference", "relation": "measures", "target": "mlsys.privacy-leakage", "statement": "measures", "conditions": "When attack advantage over a baseline is used as an empirical estimate of record-level leakage." }, { "source": "mlsys.differential-privacy", "relation": "limits", "target": "mlsys.membership-inference", "statement": "bounds the advantage of", "conditions": "At the unit defined by the neighboring-dataset relation; the bound weakens as epsilon and delta grow." }, { "source": "mlsys.model-inversion", "relation": "causes", "target": "mlsys.privacy-leakage", "statement": "causes", "conditions": "When outputs, confidence scores, or shared gradients carry enough information about sensitive attributes or inputs." }, { "source": "mlsys.dp-sgd", "relation": "implements", "target": "mlsys.differential-privacy", "statement": "implements", "conditions": "When clipping norm, noise multiplier, sampling rate, and step count are tracked by a privacy accountant." }, { "source": "mlsys.dp-sgd", "relation": "reduces", "target": "mlsys.compute-throughput", "statement": "increases demand on", "conditions": "Per-example gradient computation and clipping add compute and memory relative to standard minibatch SGD." }, { "source": "mlsys.machine-unlearning", "relation": "alternative-to", "target": "mlsys.retraining", "statement": "is a cheaper, approximate alternative to full", "conditions": "Exact unlearning equals retraining without the removed records; approximate methods trade the strength of the guarantee for lower cost." }, { "source": "mlsys.machine-unlearning", "relation": "requires", "target": "mlsys.lineage", "statement": "requires", "conditions": "To locate every model, shard, and derived artifact that was trained on the records to be removed." }, { "source": "mlsys.federated-learning", "relation": "uses", "target": "mlsys.secure-aggregation", "statement": "can use, to hide individual client updates,", "conditions": "When enough clients participate per round that the aggregate does not reveal any single client's update." }, { "source": "mlsys.secure-aggregation", "relation": "increases", "target": "mlsys.poisoning", "statement": "hinders detection of, increasing exposure to", "conditions": "Because the server sees only aggregated updates, it cannot inspect individual client updates for anomalies." }, { "source": "mlsys.least-privilege", "relation": "limits", "target": "mlsys.authorization", "statement": "constrains grants made by", "conditions": "When permissions are scoped to the task and expire or are revoked when the task ends." }, { "source": "mlsys.least-privilege", "relation": "mitigates", "target": "mlsys.prompt-injection", "statement": "limits the consequences of", "conditions": "When a model-driven component processes untrusted content and can act only through narrowly scoped credentials and tools." }, { "source": "mlsys.confidential-computing", "relation": "specializes", "target": "mlsys.isolation", "statement": "is a hardware-attested form of", "conditions": "When the trusted execution environment's hardware and firmware are trusted and the host operator is not." }, { "source": "mlsys.remote-attestation", "relation": "enables", "target": "mlsys.confidential-computing", "statement": "establishes trust in", "conditions": "When the verifier checks attestation evidence against reference values before releasing keys, data, or model weights." }, { "source": "mlsys.side-channel", "relation": "limits", "target": "mlsys.isolation", "statement": "can bypass", "conditions": "When tenants share accelerators, caches, memory, or interconnect whose timing or contention is observable across the boundary." }, { "source": "mlsys.operational-carbon", "relation": "part-of", "target": "mlsys.carbon", "statement": "is a component of", "conditions": "Lifecycle carbon sums operational and embodied components under one declared accounting boundary." }, { "source": "mlsys.embodied-carbon", "relation": "part-of", "target": "mlsys.carbon", "statement": "is a component of", "conditions": "Lifecycle carbon sums operational and embodied components under one declared accounting boundary." }, { "source": "mlsys.carbon-intensity", "relation": "increases", "target": "mlsys.operational-carbon", "statement": "scales", "conditions": "Operational carbon is energy consumed times the carbon intensity of supply at the time and place of consumption." }, { "source": "mlsys.carbon-aware-scheduling", "relation": "uses", "target": "mlsys.carbon-intensity", "statement": "exploits variation in", "conditions": "When workloads are deferrable or relocatable and intensity varies materially across hours or regions." }, { "source": "mlsys.carbon-aware-scheduling", "relation": "trades-off", "target": "mlsys.slo", "statement": "trades off against", "conditions": "When shifting work in time or place delays completion or adds latency beyond a stated objective." }, { "source": "mlsys.energy-efficiency", "relation": "reduces", "target": "mlsys.energy", "statement": "lowers, for a fixed amount of work,", "conditions": "When useful work and energy are measured over the same system boundary and workload." }, { "source": "mlsys.emission-scopes", "relation": "determines", "target": "mlsys.embodied-carbon", "statement": "classifies as value-chain (Scope 3)", "conditions": "For an operator buying hardware, manufacturing emissions fall in Scope 3 while purchased electricity falls in Scope 2." }, { "source": "mlsys.lifecycle-assessment", "relation": "measures", "target": "mlsys.embodied-carbon", "statement": "estimates", "conditions": "When the assessment's goal, scope, and functional unit are stated and manufacturer data are available or proxied." }, { "source": "mlsys.energy", "relation": "increases", "target": "mlsys.water-footprint", "statement": "increases", "conditions": "Through on-site evaporative cooling and off-site water used in electricity generation, both of which scale with energy use." }, { "source": "mlsys.water-footprint", "relation": "trades-off", "target": "mlsys.carbon-aware-scheduling", "statement": "can conflict with", "conditions": "When low-carbon hours or regions coincide with high cooling water demand, such as hot sunny afternoons." }, { "source": "mlsys.hardware-lifetime", "relation": "reduces", "target": "mlsys.embodied-carbon", "statement": "amortizes, lowering per-unit-work", "conditions": "When embodied emissions are attributed per year or per unit of work over the asset's service life." }, { "source": "mlsys.hardware-lifetime", "relation": "trades-off", "target": "mlsys.energy-efficiency", "statement": "trades off against", "conditions": "Keeping older hardware saves embodied emissions and capital but forgoes newer generations' work per joule." }, { "source": "mlsys.hardware-lifetime", "relation": "reduces", "target": "mlsys.e-waste", "statement": "lowers the annual rate of", "conditions": "For a fleet of fixed size, shorter refresh cycles retire more equipment per year." }, { "source": "mlsys.tco", "relation": "part-of", "target": "mlsys.unit-cost", "statement": "supplies the numerator of", "conditions": "When the TCO window and the count of accepted outcomes cover the same period and system boundary." }, { "source": "mlsys.hardware-lifetime", "relation": "determines", "target": "mlsys.tco", "statement": "sets the amortization period in", "conditions": "Capital cost enters TCO through amortization over the assumed service life." }, { "source": "mlsys.cost-per-token", "relation": "measures", "target": "mlsys.unit-cost", "statement": "approximates", "conditions": "Only when tokens per accepted outcome are stable; longer reasoning, retries, or context resends make cheap tokens an expensive outcome." }, { "source": "mlsys.batching", "relation": "reduces", "target": "mlsys.cost-per-token", "statement": "reduces", "conditions": "When larger or continuous batches raise tokens per accelerator-second without violating latency objectives." }, { "source": "mlsys.model-routing", "relation": "requires", "target": "mlsys.calibration", "statement": "depends on", "conditions": "When routers or cascade gates escalate on predicted quality or confidence, which must be calibrated to the request distribution." }, { "source": "mlsys.cost-attribution", "relation": "enables", "target": "mlsys.unit-cost", "statement": "enables", "conditions": "When shared spend is allocated to the workloads that produce the counted outcomes by a declared allocation rule." }, { "source": "mlsys.safety-case", "relation": "requires", "target": "mlsys.hazard-analysis", "statement": "argues only over hazards from", "conditions": "A safety case is complete only relative to the hazards identified for its stated operating domain." }, { "source": "mlsys.stpa", "relation": "specializes", "target": "mlsys.hazard-analysis", "statement": "is a method of", "conditions": "STPA addresses hazards from unsafe interactions and control flaws that component-failure methods can miss." }, { "source": "mlsys.fmea", "relation": "specializes", "target": "mlsys.hazard-analysis", "statement": "is a method of", "conditions": "FMEA is bottom-up over component failures and does not by itself capture hazards without a failed component." }, { "source": "mlsys.operating-domain", "relation": "limits", "target": "mlsys.functional-insufficiency", "statement": "scopes the triggering conditions for", "conditions": "SOTIF analysis applies to known and unknown triggering conditions inside the declared operating domain." }, { "source": "mlsys.distribution-shift", "relation": "causes", "target": "mlsys.functional-insufficiency", "statement": "can expose", "conditions": "Holds when deployment conditions fall outside those a learned component's training data covered." }, { "source": "mlsys.control-barrier-function", "relation": "enables", "target": "mlsys.safety-filter", "statement": "provides the certificate for", "conditions": "Guarantees depend on an accurate dynamics model, bounded disturbance, and a feasible filtering problem at each tick." }, { "source": "mlsys.safety-filter", "relation": "implements", "target": "mlsys.runtime-assurance", "statement": "implements", "conditions": "The filter must run independently of, and with authority over, the component it constrains." }, { "source": "mlsys.simplex-architecture", "relation": "implements", "target": "mlsys.runtime-assurance", "statement": "implements", "conditions": "Switching must occur while the verified controller can still recover the state." }, { "source": "mlsys.simplex-architecture", "relation": "uses", "target": "mlsys.minimal-risk-condition", "statement": "falls back toward", "conditions": "Holds when the safety controller's goal is to reach a defined minimal-risk state rather than continue the mission." }, { "source": "mlsys.stopping-envelope", "relation": "limits", "target": "mlsys.minimal-risk-condition", "statement": "bounds reachability of", "conditions": "A stop-type fallback is feasible only while the state remains inside its stopping envelope." }, { "source": "mlsys.guardrails", "relation": "detects", "target": "mlsys.prompt-injection", "statement": "partially detects", "conditions": "Classifier guardrails are probabilistic and can be evaded, so they do not replace authority boundaries." }, { "source": "mlsys.guardrails", "relation": "specializes", "target": "mlsys.runtime-assurance", "statement": "is a content-level form of", "conditions": "Applies when the safe set is a content or action policy rather than a physical state region." }, { "source": "mlsys.falsification", "relation": "uses", "target": "mlsys.scenario-based-testing", "statement": "searches the parameter space of", "conditions": "Found counterexamples are only as realistic as the scenario model and simulator." }, { "source": "mlsys.safety-case", "relation": "uses", "target": "mlsys.rare-event-validation", "statement": "draws statistical evidence from", "conditions": "Bounds hold for the sampled operating distribution and configuration, not for unmodeled conditions." }, { "source": "mlsys.neural-network-verification", "relation": "specializes", "target": "mlsys.formal-verification", "statement": "specializes", "conditions": "Exact methods scale poorly with network size; bound-propagation methods trade completeness for scale." }, { "source": "mlsys.neural-network-verification", "relation": "measures", "target": "mlsys.evasion", "statement": "certifies absence, within a bounded region, of", "conditions": "Holds only for the specified perturbation set, typically a norm ball around given inputs." }, { "source": "mlsys.xil-testing", "relation": "measures", "target": "mlsys.sim-to-real", "statement": "progressively tests", "conditions": "Each rung adds real software, timing, or hardware but still relies on simulated or recorded environments." }, { "source": "mlsys.formal-verification", "relation": "measures", "target": "mlsys.control-barrier-function", "statement": "can certify", "conditions": "Certification covers the barrier condition under the modeled dynamics, not the fidelity of the model." }, { "source": "mlsys.goal-structuring-notation", "relation": "implements", "target": "mlsys.safety-case", "statement": "expresses", "conditions": "Notation makes the argument structure reviewable; it does not make the argument sound." }, { "source": "mlsys.safety-performance-indicator", "relation": "measures", "target": "mlsys.safety-case", "statement": "monitors validity of", "conditions": "Each indicator must trace to a specific claim and have a threshold that triggers review." }, { "source": "mlsys.safety-performance-indicator", "relation": "requires", "target": "mlsys.monitoring", "statement": "is collected through", "conditions": "Holds when field telemetry captures the events the indicator counts." }, { "source": "mlsys.hazard-analysis", "relation": "determines", "target": "mlsys.safety-integrity-level", "statement": "assigns", "conditions": "Integrity levels derive from severity, exposure, and controllability of analyzed hazards." }, { "source": "mlsys.group-fairness-metric", "relation": "measures", "target": "mlsys.fairness", "statement": "operationalizes", "conditions": "A metric measures one criterion on a chosen population and grouping; other criteria remain unmeasured." }, { "source": "mlsys.fairness-incompatibility", "relation": "limits", "target": "mlsys.group-fairness-metric", "statement": "forces a choice among", "conditions": "Holds when base rates differ across groups and the predictor is imperfect." }, { "source": "mlsys.individual-fairness", "relation": "trades-off", "target": "mlsys.group-fairness-metric", "statement": "can conflict with", "conditions": "Holds when similarity under the task metric does not align with group membership." }, { "source": "mlsys.bias-mitigation", "relation": "trades-off", "target": "mlsys.metrics", "statement": "trades off against aggregate", "conditions": "Reducing a disparity often lowers aggregate accuracy or shifts error to other groups or metrics." }, { "source": "mlsys.feature-attribution", "relation": "increases", "target": "mlsys.tail-latency", "statement": "adds to", "conditions": "Holds when explanations are computed on the request path; perturbation-based methods need many extra model evaluations." }, { "source": "mlsys.counterfactual-explanation", "relation": "enables", "target": "mlsys.contestability", "statement": "supports", "conditions": "Useful for recourse only when the suggested changes are feasible and actionable for the person." }, { "source": "mlsys.interpretable-model", "relation": "alternative-to", "target": "mlsys.feature-attribution", "statement": "removes the need for post-hoc", "conditions": "Holds in high-stakes settings where an interpretable model reaches comparable accuracy." }, { "source": "mlsys.model-card", "relation": "uses", "target": "mlsys.sliced-evaluation", "statement": "reports results of", "conditions": "A model card is informative only for the conditions and groups actually evaluated." }, { "source": "mlsys.datasheet", "relation": "uses", "target": "mlsys.lineage", "statement": "summarizes, in human-readable form, provenance from", "conditions": "A datasheet summarizes provenance; machine-checkable lineage is still needed for reproducibility." }, { "source": "mlsys.automation-bias", "relation": "reduces", "target": "mlsys.human-oversight", "statement": "undermines", "conditions": "Holds when automation is usually right, error cues are weak, and reviewers face time pressure." }, { "source": "mlsys.feedback-delay", "relation": "limits", "target": "mlsys.authority-handover", "statement": "bounds, through human reaction time,", "conditions": "Takeover is safe only if human reaction and re-orientation time fits within the remaining safety margin." }, { "source": "mlsys.contestability", "relation": "part-of", "target": "mlsys.human-oversight", "statement": "extends to affected people", "conditions": "Requires a channel, decision records, and authority to reverse outcomes." }, { "source": "mlsys.impact-assessment", "relation": "part-of", "target": "mlsys.ai-risk-management", "statement": "is part of", "conditions": "Mapping risks to affected parties precedes measuring and managing them." }, { "source": "mlsys.algorithmic-audit", "relation": "uses", "target": "mlsys.model-card", "statement": "draws evidence from", "conditions": "Audits also need access to data, code, and logs; documentation alone is insufficient evidence." }, { "source": "mlsys.risk-tiered-regulation", "relation": "requires", "target": "mlsys.human-oversight", "statement": "mandates for high-risk uses", "conditions": "Applies to systems classified high-risk under the governing regulation." }, { "source": "mlsys.risk-tiered-regulation", "relation": "requires", "target": "mlsys.algorithmic-audit", "statement": "requires conformity assessment by", "conditions": "Applies to high-risk categories; obligations differ by role (provider, deployer)." }, { "source": "mlsys.frontier-safety-framework", "relation": "specializes", "target": "mlsys.ai-risk-management", "statement": "specializes for frontier models", "conditions": "Effectiveness depends on whether capability evaluations elicit the capabilities they test for." }, { "source": "mlsys.context-compaction", "relation": "reduces", "target": "mlsys.prefix-cache", "statement": "invalidates reuse in", "conditions": "Rewriting earlier context breaks prefix reuse, so compaction pays off only when saved tokens exceed recompute." }, { "source": "mlsys.context-degradation", "relation": "limits", "target": "mlsys.context", "statement": "limits useful size of", "conditions": "Holds even when the context fits the nominal window; strength varies by model and task." }, { "source": "mlsys.agent-memory", "relation": "uses", "target": "mlsys.retrieval", "statement": "is read through", "conditions": "Memory recall quality is bounded by the retrieval contract and index freshness." }, { "source": "mlsys.agent-memory", "relation": "enables", "target": "mlsys.poisoning", "statement": "can corrupt", "conditions": "Holds when untrusted content can be written into memory and later retrieved as trusted context." }, { "source": "mlsys.function-calling", "relation": "requires", "target": "mlsys.tool-contract", "statement": "is validated against", "conditions": "The harness must validate calls against the contract; schema conformance does not imply authority." }, { "source": "mlsys.model-context-protocol", "relation": "enables", "target": "mlsys.function-calling", "statement": "standardizes tool discovery for", "conditions": "Discovering a tool through a protocol does not grant authority to use it." }, { "source": "mlsys.computer-use", "relation": "increases", "target": "mlsys.irreversible-effects", "statement": "raises exposure to", "conditions": "GUI actions often lack typed preconditions, idempotency keys, or undo, unlike designed tool APIs." }, { "source": "mlsys.computer-use", "relation": "increases", "target": "mlsys.prompt-injection", "statement": "widens the surface for", "conditions": "Holds when on-screen content from untrusted sources is read as part of the agent's context." }, { "source": "mlsys.agent-harness", "relation": "produces", "target": "mlsys.trajectory", "statement": "produces and records", "conditions": "The harness, not the model, owns continuation and the authoritative trajectory record." }, { "source": "mlsys.agent-budget", "relation": "determines", "target": "mlsys.unit-cost", "statement": "caps per-trajectory", "conditions": "Budgets cap spend per trajectory; cost per accepted task also depends on success rate." }, { "source": "mlsys.approval-gate", "relation": "implements", "target": "mlsys.human-oversight", "statement": "implements", "conditions": "Effective only if approvals bind to the exact action and reviewers can judge it; see automation bias." }, { "source": "mlsys.approval-gate", "relation": "mitigates", "target": "mlsys.irreversible-effects", "statement": "guards against", "conditions": "Holds when the gate is placed before dispatch of actions classified irreversible." }, { "source": "mlsys.durable-execution", "relation": "requires", "target": "mlsys.idempotency", "statement": "relies on", "conditions": "Resuming after a crash between dispatch and record can re-issue a step, which is safe only for idempotent or deduplicated effects." }, { "source": "mlsys.deterministic-replay", "relation": "uses", "target": "mlsys.durable-execution", "statement": "reuses the log of", "conditions": "Replay is exact only if every nondeterministic input, including model samples and tool results, was recorded." }, { "source": "mlsys.suspend-resume", "relation": "enables", "target": "mlsys.approval-gate", "statement": "enables long waits for", "conditions": "Resumption must revalidate state and authority that may have changed during the wait." }, { "source": "mlsys.suspend-resume", "relation": "requires", "target": "mlsys.fencing", "statement": "requires", "conditions": "Holds when a resumed worker could race a stale one that still believes it owns the trajectory." }, { "source": "mlsys.multi-agent-orchestration", "relation": "causes", "target": "mlsys.coordination-failure", "statement": "can introduce", "conditions": "Each delegation and handoff adds a point where specification or context can be lost." }, { "source": "mlsys.multi-agent-orchestration", "relation": "requires", "target": "mlsys.baseline", "statement": "must be justified against a single-agent", "conditions": "Comparisons should match total compute or token budget." }, { "source": "mlsys.shared-agent-state", "relation": "uses", "target": "mlsys.fencing", "statement": "uses", "conditions": "Holds when leases can expire while a slow agent still attempts writes." }, { "source": "mlsys.correlated-agent-errors", "relation": "limits", "target": "mlsys.multi-agent-orchestration", "statement": "limits the benefit of", "conditions": "Holds when agents share a base model, prompts, or retrieved evidence." }, { "source": "mlsys.sensor-fusion", "relation": "requires", "target": "mlsys.time-synchronization", "statement": "requires", "conditions": "Timestamp error translates to spatial error proportional to relative motion." }, { "source": "mlsys.sensor-fusion", "relation": "requires", "target": "mlsys.sensing", "statement": "requires extrinsic calibration from", "conditions": "Fused estimates inherit calibration errors between sensor frames." }, { "source": "mlsys.robot-perception", "relation": "increases", "target": "mlsys.observation-age", "statement": "adds inference latency to", "conditions": "Inference time adds directly to the age of the state used by planning and control." }, { "source": "mlsys.recursive-filtering", "relation": "implements", "target": "mlsys.state-estimation", "statement": "implements", "conditions": "Kalman-family filters assume approximately Gaussian, well-modeled noise; violations make reported covariance overconfident." }, { "source": "mlsys.slam", "relation": "specializes", "target": "mlsys.state-estimation", "statement": "specializes", "conditions": "Drift accumulates without loop closure or external references." }, { "source": "mlsys.model-predictive-control", "relation": "uses", "target": "mlsys.world-model", "statement": "can use as its prediction model a", "conditions": "Plans exploit model errors, so performance depends on accuracy in the regions the optimizer visits." }, { "source": "mlsys.motion-planning", "relation": "requires", "target": "mlsys.trajectory-feasibility", "statement": "must satisfy", "conditions": "Geometric planners need a later step to enforce dynamic and actuation limits." }, { "source": "mlsys.model-predictive-control", "relation": "requires", "target": "mlsys.wcet", "statement": "requires a solver WCET within its control period, i.e. a", "conditions": "Holds when each optimization must finish within the control period; warm starts and horizon length trade quality for time." }, { "source": "mlsys.model-predictive-control", "relation": "specializes", "target": "mlsys.feedback-control", "statement": "is a form of", "conditions": "Re-solving from each new state estimate closes the loop." }, { "source": "mlsys.real-time-scheduling", "relation": "requires", "target": "mlsys.wcet", "statement": "takes as input", "conditions": "Schedulability results are valid only if WCET bounds hold on the deployed platform." }, { "source": "mlsys.mixed-criticality", "relation": "enables", "target": "mlsys.runtime-assurance", "statement": "protects the timing of", "conditions": "Holds when the safety monitor shares hardware with learned components that can overrun or contend for memory." }, { "source": "mlsys.mixed-criticality", "relation": "requires", "target": "mlsys.isolation", "statement": "requires temporal and spatial", "conditions": "Shared caches, memory controllers, and buses can break isolation that address-space separation provides." }, { "source": "mlsys.vla-model", "relation": "increases", "target": "mlsys.observation-age", "statement": "increases", "conditions": "Holds when billion-parameter inference on robot compute runs slower than the control loop." }, { "source": "mlsys.vla-model", "relation": "uses", "target": "mlsys.action-chunking", "statement": "uses, to decouple control rate from inference rate,", "conditions": "Chunks executed open-loop delay reaction to new observations until the next inference." }, { "source": "mlsys.teleoperation-data", "relation": "enables", "target": "mlsys.imitation-learning", "statement": "supplies demonstrations for", "conditions": "Policy quality is bounded by demonstration coverage, consistency, and action-label timing." }, { "source": "mlsys.domain-randomization", "relation": "enables", "target": "mlsys.sim-to-real", "statement": "improves", "conditions": "Holds when the randomized ranges cover the real parameters without making the task unlearnable." }, { "source": "mlsys.thread-hierarchy", "relation": "uses", "target": "mlsys.simt", "statement": "is executed through", "conditions": "Warps of a thread block are scheduled together on one streaming multiprocessor." }, { "source": "mlsys.memory-coalescing", "relation": "determines", "target": "mlsys.memory-bandwidth", "statement": "determines achieved", "conditions": "Uncoalesced or bank-conflicted accesses waste transactions, so effective bandwidth falls below peak." }, { "source": "mlsys.async-data-movement", "relation": "implements", "target": "mlsys.communication-overlap", "statement": "applies within a device the idea of", "conditions": "Loads into on-chip memory proceed while matrix units compute on the previous tile." }, { "source": "mlsys.matrix-engine-programming", "relation": "enables", "target": "mlsys.compute-throughput", "statement": "is required to reach peak", "conditions": "Peak matrix throughput is available only to instructions with supported shapes, layouts, and precisions." }, { "source": "mlsys.thread-hierarchy", "relation": "implements", "target": "mlsys.tiling", "statement": "maps output tiles onto thread blocks in", "conditions": "Each thread block typically computes one output tile staged through shared memory." }, { "source": "mlsys.embedding-table-sharding", "relation": "enables", "target": "mlsys.recommendation-model", "statement": "makes trainable", "conditions": "Embedding tables exceed one device's memory and must be partitioned across devices." }, { "source": "mlsys.embedding-table-sharding", "relation": "requires", "target": "mlsys.collectives", "statement": "requires all-to-all", "conditions": "Lookups for a batch touch rows held on other devices, so activations are exchanged." }, { "source": "mlsys.embedding-cache", "relation": "increases", "target": "mlsys.memory-capacity", "statement": "extends effective", "conditions": "Lookup skew must concentrate accesses on a small hot set of rows." }, { "source": "mlsys.parameter-server", "relation": "uses", "target": "mlsys.bounded-staleness", "statement": "commonly operates under", "conditions": "Workers may read parameters that lag the latest updates by a bounded number of steps." }, { "source": "mlsys.latency-budget", "relation": "limits", "target": "mlsys.ranking-funnel", "statement": "bounds the per-stage cost of", "conditions": "Each stage receives a share of the end-to-end budget and a candidate count it can afford to score." }, { "source": "mlsys.ranking-funnel", "relation": "uses", "target": "mlsys.ann", "statement": "uses for candidate generation", "conditions": "The first stage retrieves candidates by approximate similarity before learned ranking." }, { "source": "mlsys.multimodal-model", "relation": "uses", "target": "mlsys.modality-encoder", "statement": "is composed with", "conditions": "Non-text inputs are encoded into tokens the shared backbone attends over." }, { "source": "mlsys.encoder-disaggregation", "relation": "reduces", "target": "mlsys.tail-latency", "statement": "reduces", "conditions": "Large media inputs would otherwise block token generation for requests sharing the batch." }, { "source": "mlsys.auto-parallelization", "relation": "produces", "target": "mlsys.hybrid-parallel", "statement": "derives", "conditions": "The compiler's cost model must reflect the target device mesh's compute and link bandwidths for the chosen plan to be competitive." }, { "source": "mlsys.sharding-propagation", "relation": "implements", "target": "mlsys.auto-parallelization", "statement": "implements", "conditions": "Holds when users supply seed annotations and the compiler propagates layouts through operators with known sharding rules." }, { "source": "mlsys.sharding-propagation", "relation": "produces", "target": "mlsys.collectives", "statement": "inserts", "conditions": "Collectives are inserted wherever an operator's input layouts differ from what its sharded implementation requires." }, { "source": "mlsys.sharding-propagation", "relation": "requires", "target": "mlsys.device-mesh", "statement": "is defined over the axes of", "conditions": "Tensor dimensions are sharded over named mesh axes, so mesh shape bounds which layouts are expressible." }, { "source": "mlsys.function-transformations", "relation": "implements", "target": "mlsys.autodiff", "statement": "exposes as a transform", "conditions": "Holds when programs are pure functions so differentiation composes with vectorization and compilation." }, { "source": "mlsys.function-transformations", "relation": "requires", "target": "mlsys.tracing-jit", "statement": "relies on", "conditions": "Transformations are applied to a traced intermediate representation, so Python side effects and data-dependent control flow are not captured." }, { "source": "mlsys.task-actor-runtime", "relation": "enables", "target": "mlsys.rl-rollout", "statement": "orchestrates", "conditions": "Applies when rollout workers, trainers, and reward models run as separate stateful actors exchanging data through the runtime." }, { "source": "mlsys.single-vs-multi-controller", "relation": "determines", "target": "mlsys.task-actor-runtime", "statement": "is a design axis of", "conditions": "A single controller simplifies heterogeneous pipelines but must keep per-step dispatch off the critical path to scale." }, { "source": "mlsys.analytical-performance-model", "relation": "uses", "target": "mlsys.roofline", "statement": "composes", "conditions": "Per-operator roofline bounds are summed or overlapped with communication and queueing terms under stated overlap assumptions." }, { "source": "mlsys.analytical-performance-model", "relation": "uses", "target": "mlsys.alpha-beta-model", "statement": "composes", "conditions": "Communication terms use per-collective latency and bandwidth parameters measured or specified for the target fabric." }, { "source": "mlsys.model-fidelity", "relation": "measures", "target": "mlsys.analytical-performance-model", "statement": "bounds trust in", "conditions": "Predictions are credible only within the configurations where validation error against measurement has been reported." }, { "source": "mlsys.performance-simulation", "relation": "alternative-to", "target": "mlsys.analytical-performance-model", "statement": "trades speed for detail against", "conditions": "Simulation captures contention and scheduling effects closed forms omit, at much higher evaluation cost per configuration." }, { "source": "mlsys.post-training-quantization", "relation": "specializes", "target": "mlsys.quantization", "statement": "applies", "conditions": "Holds when the calibration set is representative of deployment inputs; otherwise chosen ranges misfit and accuracy drops." }, { "source": "mlsys.weight-only-quantization", "relation": "specializes", "target": "mlsys.post-training-quantization", "statement": "is usually a form of", "conditions": "Holds for GPTQ- and AWQ-class methods that set weight codes from calibration data without retraining." }, { "source": "mlsys.activation-outliers", "relation": "limits", "target": "mlsys.quantization", "statement": "can degrade the accuracy of", "conditions": "Applies to per-tensor activation quantization of large transformers; per-channel handling or scale migration mitigates it." }, { "source": "mlsys.kv-cache-quantization", "relation": "reduces", "target": "mlsys.kv-cache", "statement": "shrinks", "conditions": "Capacity gains translate into throughput only when serving is KV-memory-bound and attention kernels read the quantized format directly." }, { "source": "mlsys.nm-sparsity", "relation": "requires", "target": "mlsys.matrix-unit", "statement": "requires support in", "conditions": "Speedup materializes only on matrix engines with sparse-operand instructions for the specific N:M ratio." }, { "source": "mlsys.nm-sparsity", "relation": "specializes", "target": "mlsys.sparsity", "statement": "specializes", "conditions": "Constrains sparsity to a fixed per-group pattern so hardware can index nonzeros with small metadata." }, { "source": "mlsys.kv-cache-offload", "relation": "enables", "target": "mlsys.prefix-cache", "statement": "increases the capacity and hit rate of", "conditions": "Pays off when reload time from host or SSD is shorter than recomputing the prefix's prefill." }, { "source": "mlsys.pd-disaggregation", "relation": "requires", "target": "mlsys.kv-cache-transfer", "statement": "requires", "conditions": "Disaggregation is profitable only when KV transfer between pools is faster than the decode-latency gain it buys." }, { "source": "mlsys.compound-ai-system", "relation": "uses", "target": "mlsys.model-routing", "statement": "composes", "conditions": "Holds when the program chooses among models per step, so routing decisions are optimized against end-to-end quality and cost." }, { "source": "mlsys.rag", "relation": "specializes", "target": "mlsys.compound-ai-system", "statement": "is a kind of", "conditions": "Retrieval-augmented generation is the two-component case of a retriever feeding one model call." }, { "source": "mlsys.lm-program-optimization", "relation": "determines", "target": "mlsys.compound-ai-system", "statement": "tunes the prompts and weights of", "conditions": "Requires a declared program structure and an end-to-end metric evaluable on a training set of inputs." }, { "source": "mlsys.multi-adapter-serving", "relation": "uses", "target": "mlsys.lora", "statement": "serves many instances of", "conditions": "Holds when adapters share one frozen base model so base weights are loaded once and only low-rank factors differ per request." }, { "source": "mlsys.multi-adapter-serving", "relation": "specializes", "target": "mlsys.multi-model-serving", "statement": "specializes", "conditions": "Applies when hosted variants differ only by adapters, letting requests for different variants share one batch." }, { "source": "mlsys.graph-vector-index", "relation": "implements", "target": "mlsys.ann", "statement": "implements", "conditions": "Best recall-latency trade-off when the full graph and vectors fit in memory." }, { "source": "mlsys.quantized-vector-index", "relation": "implements", "target": "mlsys.ann", "statement": "implements", "conditions": "Preferred when memory per vector is the binding constraint, accepting lower recall or a reranking pass on exact vectors." }, { "source": "mlsys.quantized-vector-index", "relation": "alternative-to", "target": "mlsys.graph-vector-index", "statement": "trades memory against recall versus", "conditions": "Compared at equal query latency on the same corpus and recall target." }, { "source": "mlsys.filtered-vector-search", "relation": "specializes", "target": "mlsys.ann", "statement": "is a predicate-constrained form of", "conditions": "Recall or cost degrades most when the predicate is selective and uncorrelated with vector proximity." }, { "source": "mlsys.graph-vector-index", "relation": "limits", "target": "mlsys.index-freshness", "statement": "update and delete cost bounds attainable", "conditions": "Deletes and in-place updates degrade graph connectivity unless repaired or periodically rebuilt." }, { "source": "mlsys.numerical-nondeterminism", "relation": "reduces", "target": "mlsys.experiment-reproducibility", "statement": "limits", "conditions": "Bitwise reproducibility requires fixing kernels, reduction order, parallel layout, and hardware, not only random seeds." }, { "source": "mlsys.accumulation-precision", "relation": "determines", "target": "mlsys.numerical-nondeterminism", "statement": "sets the magnitude of", "conditions": "Order-dependent rounding differences grow as accumulation precision falls and reduction length grows." }, { "source": "mlsys.batch-invariance", "relation": "mitigates", "target": "mlsys.numerical-nondeterminism", "statement": "removes the batch-composition source of", "conditions": "Achieved by fixing reduction strategies independent of batch size, typically at some kernel throughput cost." }, { "source": "mlsys.batch-invariance", "relation": "enables", "target": "mlsys.rl-rollout", "statement": "aligns inference numerics with training for", "conditions": "Matters when rollout log-probabilities from the serving engine are reused in on-policy training objectives." }, { "source": "mlsys.elastic-training", "relation": "requires", "target": "mlsys.checkpoint-resharding", "statement": "requires", "conditions": "Holds whenever a restart changes world size or parallel layout relative to the saved checkpoint." }, { "source": "mlsys.checkpoint-resharding", "relation": "uses", "target": "mlsys.state-sharding", "statement": "remaps between layouts of", "conditions": "Requires checkpoints that record each shard's global tensor coordinates rather than rank-local layouts." }, { "source": "mlsys.batch-data-processing", "relation": "enables", "target": "mlsys.deduplication", "statement": "executes at corpus scale", "conditions": "Applies when the corpus exceeds one machine, so near-duplicate detection is partitioned and shuffled across workers." }, { "source": "mlsys.batch-data-processing", "relation": "enables", "target": "mlsys.feature-backfill", "statement": "executes", "conditions": "Backfills over long histories run as bounded batch jobs whose logic must match the serving feature definition." }, { "source": "mlsys.distributed-tracing", "relation": "part-of", "target": "mlsys.observability", "statement": "provides request-level evidence within", "conditions": "Requires trace context to be propagated across every service and asynchronous hop the request traverses." }, { "source": "mlsys.llm-call-tracing", "relation": "specializes", "target": "mlsys.distributed-tracing", "statement": "specializes", "conditions": "Adds model-specific span attributes such as token counts, model version, and cost under a shared semantic convention." }, { "source": "mlsys.llm-call-tracing", "relation": "measures", "target": "mlsys.agent-harness", "statement": "instruments", "conditions": "Each loop iteration, model call, and tool call is emitted as a child span of the task's root span." }, { "source": "mlsys.evaluation-harness", "relation": "implements", "target": "mlsys.benchmark", "statement": "operationalizes", "conditions": "Comparability holds only when prompt templates, decoding settings, and scorer versions are pinned with the results." }, { "source": "mlsys.evaluation-harness", "relation": "uses", "target": "mlsys.experiment-tracking", "statement": "records results into", "conditions": "Holds when each run logs checkpoint, suite version, and configuration alongside scores." }, { "source": "mlsys.energy-measurement", "relation": "measures", "target": "mlsys.energy", "statement": "determines the reported value of", "conditions": "Reported energy is comparable only across measurements with the same boundary, sensor class, and sampling rate." }, { "source": "mlsys.idle-dynamic-power", "relation": "determines", "target": "mlsys.energy-measurement", "statement": "sets attribution rules for", "conditions": "Matters when hardware is shared or partly idle, so idle power must be charged by an explicit allocation rule." }, { "source": "mlsys.he-inference", "relation": "alternative-to", "target": "mlsys.confidential-computing", "statement": "trades compute overhead for hardware trust versus", "conditions": "Homomorphic encryption avoids trusting server hardware but costs orders of magnitude more compute than enclave execution." }, { "source": "mlsys.secure-computation-inference", "relation": "alternative-to", "target": "mlsys.he-inference", "statement": "trades communication for computation versus", "conditions": "Hybrid protocols use homomorphic encryption for linear layers and secret sharing or garbled circuits for nonlinear ones." }, { "source": "mlsys.secure-computation-inference", "relation": "mitigates", "target": "mlsys.privacy-leakage", "statement": "prevents input disclosure in", "conditions": "Protects inputs during computation but not leakage from the revealed outputs themselves." }, { "source": "mlsys.red-teaming", "relation": "detects", "target": "mlsys.jailbreak", "statement": "discovers", "conditions": "Coverage depends on attacker diversity and budget, so finding no failures does not establish their absence." }, { "source": "mlsys.red-teaming", "relation": "requires", "target": "mlsys.threat-model", "statement": "must be scoped by", "conditions": "The adversary capabilities, interfaces, and harms probed must be declared for findings to be interpretable." }, { "source": "mlsys.dangerous-capability-eval", "relation": "part-of", "target": "mlsys.frontier-safety-framework", "statement": "supplies the threshold tests that trigger safeguards in", "conditions": "Holds when the framework ties stated capability thresholds to required mitigations before further training or deployment." }, { "source": "mlsys.dangerous-capability-eval", "relation": "uses", "target": "mlsys.red-teaming", "statement": "uses elicitation from", "conditions": "Capability estimates are lower bounds unless elicitation, including expert red teaming, is strong." }, { "source": "mlsys.efficiency-frontier", "relation": "specializes", "target": "mlsys.pareto-frontier", "statement": "specializes", "conditions": "The objectives are model quality and a stated cost measure on a stated platform." }, { "source": "mlsys.energy-per-inference", "relation": "measures", "target": "mlsys.energy-efficiency", "statement": "is the per-input reciprocal of", "conditions": "Both are measured within the same declared system boundary." }, { "source": "mlsys.requirements", "relation": "determines", "target": "mlsys.placement", "statement": "determine feasible", "conditions": "Latency, privacy, connectivity, and power requirements rule out some device, edge, or datacenter placements." }, { "source": "mlsys.release-gate", "relation": "requires", "target": "mlsys.baseline", "statement": "compares candidates against", "conditions": "A candidate advances only if it beats or matches the incumbent baseline on the gated metrics under the same protocol." }, { "source": "mlsys.baseline", "relation": "part-of", "target": "mlsys.online-experiment", "statement": "serves as the control arm of", "conditions": "The incumbent system or model is served to the control group during the experiment." }, { "source": "mlsys.benchmark", "relation": "requires", "target": "mlsys.workload", "statement": "is only meaningful for a stated", "conditions": "Benchmark results transfer only to deployments whose input, request, and model distributions match the benchmark workload." }, { "source": "mlsys.workload", "relation": "uses", "target": "mlsys.workload-trace", "statement": "records an empirical sample of", "conditions": "The trace window covers the arrival, size, and mix variation that matters for the decision being evaluated." }, { "source": "mlsys.reward-model", "relation": "causes", "target": "mlsys.reward-hacking", "statement": "can invite", "conditions": "Policies optimized hard against a learned reward model can exploit its errors, scoring higher while true quality falls." }, { "source": "mlsys.monitoring", "relation": "produces", "target": "mlsys.sli", "statement": "computes", "conditions": "Monitoring records the events and counts over the population and window the indicator definition specifies." }, { "source": "mlsys.sli", "relation": "measures", "target": "mlsys.tail-latency", "statement": "commonly measures, as a percentile,", "conditions": "Latency indicators are computed as a high percentile over a stated request population and window." }, { "source": "mlsys.sli", "relation": "measures", "target": "mlsys.availability", "statement": "commonly measures", "conditions": "Availability indicators count successful requests or uptime over a defined population and window." }, { "source": "mlsys.error-budget", "relation": "determines", "target": "mlsys.release-gate", "statement": "can freeze releases through", "conditions": "Teams agree that an exhausted budget blocks or slows promotion of new versions." }, { "source": "mlsys.error-budget", "relation": "determines", "target": "mlsys.alerting", "statement": "sets burn-rate thresholds for", "conditions": "Alerts fire when the budget is being consumed faster than the window allows." }, { "source": "mlsys.latency-budget", "relation": "limits", "target": "mlsys.reasoning-budget", "statement": "caps the intermediate tokens allowed by", "conditions": "Interactive requests must answer within a fixed end-to-end latency target, so extra reasoning tokens consume the same budget." }, { "source": "mlsys.activation-recompute", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "reduces the peak", "conditions": "Retained activations dominate training memory and spare compute is available to recompute them." }, { "source": "mlsys.kv-cache", "relation": "increases", "target": "mlsys.memory-footprint", "statement": "adds per-request state to", "conditions": "Autoregressive serving retains keys and values for every in-flight token; the term grows with batch size and context length." }, { "source": "mlsys.workload-trace", "relation": "measures", "target": "mlsys.arrival-burstiness", "statement": "reveals the", "conditions": "Inter-arrival times are recorded at fine enough resolution to show bursts rather than only averages." }, { "source": "mlsys.performance-simulation", "relation": "uses", "target": "mlsys.workload-trace", "statement": "replays", "conditions": "Trace-driven simulation is used to compare scheduling, capacity, or serving policies on realistic demand." }, { "source": "mlsys.cost-sensitive-decision", "relation": "requires", "target": "mlsys.calibration", "statement": "requires calibrated probabilities from", "conditions": "Expected-cost thresholds are computed from predicted probabilities, which must match observed frequencies on the deployment distribution." }, { "source": "mlsys.cost-sensitive-decision", "relation": "determines", "target": "mlsys.selective-prediction", "statement": "sets the abstention threshold in", "conditions": "Deferring to a human or fallback has a known cost that can be compared with the expected cost of each error type." }, { "source": "mlsys.tokenization", "relation": "determines", "target": "mlsys.context", "statement": "sets how much text fits in", "conditions": "For a fixed window, a tokenizer that produces more tokens per word admits less source text." }, { "source": "mlsys.linear-model", "relation": "alternative-to", "target": "mlsys.tree-ensemble", "statement": "is an alternative to", "conditions": "Both are standard choices for tabular supervised prediction; trees capture interactions linear scores need engineered features for." }, { "source": "mlsys.linear-model", "relation": "specializes", "target": "mlsys.interpretable-model", "statement": "is, when sparse, a kind of", "conditions": "Holds when features are meaningful and few enough that coefficients can be read directly." }, { "source": "mlsys.tree-ensemble", "relation": "uses", "target": "mlsys.features", "statement": "splits on", "conditions": "Trees operate on tabular features without requiring scaling; feature engineering still determines what interactions are available." }, { "source": "mlsys.kernel-method", "relation": "increases", "target": "mlsys.memory-footprint", "statement": "has a quadratic training", "conditions": "Exact training forms or stores an n-by-n kernel matrix, so memory grows with the square of the training set size unless approximated." }, { "source": "mlsys.kernel-method", "relation": "determines", "target": "mlsys.inductive-bias", "statement": "encodes its inductive bias through the choice of kernel in", "conditions": "The kernel function fixes which similarity structure the model can exploit." }, { "source": "mlsys.nearest-neighbor", "relation": "uses", "target": "mlsys.embeddings", "statement": "searches over", "conditions": "Neighbors are found by distance in a learned or engineered vector space whose quality determines result quality." }, { "source": "mlsys.neural-network", "relation": "uses", "target": "mlsys.matrix-multiply", "statement": "spends most compute in", "conditions": "Dense and attention layers dominate; small or irregular layers can be bound by other operations." }, { "source": "mlsys.convolution", "relation": "implements", "target": "mlsys.inductive-bias", "statement": "encodes translation equivariance and locality as an", "conditions": "Weights are shared across positions, so the model assumes local patterns recur across the input." }, { "source": "mlsys.position-encoding", "relation": "limits", "target": "mlsys.context", "statement": "bounds the usable length of", "conditions": "Without extension, sequences longer than the positions seen in training degrade quality." }, { "source": "mlsys.transformer", "relation": "uses", "target": "mlsys.normalization-layer", "statement": "stabilizes residual blocks with", "conditions": "Each block applies layer or RMS normalization around attention and feed-forward sublayers." }, { "source": "mlsys.transformer", "relation": "uses", "target": "mlsys.position-encoding", "statement": "injects order with", "conditions": "Attention alone is permutation equivariant, so the architecture adds absolute, relative, or rotary positions." }, { "source": "mlsys.transformer", "relation": "specializes", "target": "mlsys.neural-network", "statement": "is a kind of", "conditions": "The architecture is a differentiable composition trained by gradient descent." }, { "source": "mlsys.ranking-funnel", "relation": "uses", "target": "mlsys.recommendation-model", "statement": "runs in its ranking stages a", "conditions": "Candidate generation narrows the set before costly deep ranking models score it within the latency budget." }, { "source": "mlsys.depthwise-separable-convolution", "relation": "specializes", "target": "mlsys.convolution", "statement": "is a factorized kind of", "conditions": "A standard convolution is split into depthwise and pointwise steps." }, { "source": "mlsys.depthwise-separable-convolution", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "cuts parameter storage in", "conditions": "Parameter memory is a significant share of the deployed footprint, as on mobile and embedded targets." }, { "source": "mlsys.recurrence", "relation": "alternative-to", "target": "mlsys.attention", "statement": "is an alternative to", "conditions": "Both mix information across sequence positions; recurrence keeps fixed state but serializes time, attention parallelizes but grows cost with length." }, { "source": "mlsys.normalization-layer", "relation": "mitigates", "target": "mlsys.training-instability", "statement": "mitigates", "conditions": "Deep networks whose activation scales would otherwise drift with depth or batch composition." }, { "source": "mlsys.expert-routing", "relation": "part-of", "target": "mlsys.mixture-of-experts", "statement": "is the gating component of", "conditions": "The layer routes each token to a small subset of experts." }, { "source": "mlsys.expert-capacity", "relation": "part-of", "target": "mlsys.mixture-of-experts", "statement": "is a buffer-size parameter of", "conditions": "Experts process fixed-size per-batch buffers so computation shapes stay static." }, { "source": "mlsys.expert-capacity", "relation": "increases", "target": "mlsys.all-to-all", "statement": "increases padded token volume in", "conditions": "A higher capacity factor allocates larger per-expert buffers that are exchanged whether or not they are full." }, { "source": "mlsys.load-balancing-loss", "relation": "mitigates", "target": "mlsys.stragglers", "statement": "reduces hot-expert imbalance behind", "conditions": "Under expert parallelism, an overloaded expert's device finishes last and holds back the step." }, { "source": "mlsys.load-balancing-loss", "relation": "part-of", "target": "mlsys.mixture-of-experts", "statement": "is an auxiliary objective in training of", "conditions": "Routing is learned and can otherwise collapse onto a few experts." }, { "source": "mlsys.active-parameters", "relation": "determines", "target": "mlsys.training-compute-estimate", "statement": "sets the parameter count used in", "conditions": "For conditionally computed models, the six-operations-per-parameter-per-token estimate uses active rather than total parameters." }, { "source": "mlsys.early-exit", "relation": "requires", "target": "mlsys.calibration", "statement": "relies on confidence quality assessed by", "conditions": "Exit decisions threshold an intermediate confidence; overconfident exits return wrong answers early." }, { "source": "mlsys.early-exit", "relation": "reduces", "target": "mlsys.energy-per-inference", "statement": "reduces average", "conditions": "Many inputs are easy enough to exit at an early classifier and the exit overhead is small." }, { "source": "mlsys.sliding-window-attention", "relation": "specializes", "target": "mlsys.attention", "statement": "is a restricted kind of", "conditions": "Each position attends only within a window or fixed sparse pattern." }, { "source": "mlsys.sliding-window-attention", "relation": "reduces", "target": "mlsys.kv-cache", "statement": "caps the size of", "conditions": "During decoding only the most recent window of keys and values must be retained." }, { "source": "mlsys.linear-attention", "relation": "specializes", "target": "mlsys.attention", "statement": "is a kernelized variant of", "conditions": "Softmax similarity is replaced by a feature-map inner product, changing model quality." }, { "source": "mlsys.linear-attention", "relation": "alternative-to", "target": "mlsys.sliding-window-attention", "statement": "is an alternative to", "conditions": "Both reduce attention cost growth for long sequences, trading different quality losses." }, { "source": "mlsys.state-space-model", "relation": "alternative-to", "target": "mlsys.attention", "statement": "is an alternative to", "conditions": "Both serve as the sequence-mixing layer; state-space layers trade exact long-range lookup for linear cost and fixed state." }, { "source": "mlsys.state-space-model", "relation": "reduces", "target": "mlsys.kv-cache", "statement": "eliminates the growing", "conditions": "Inference carries a constant-size state per layer instead of keys and values for every prior token." }, { "source": "mlsys.grouped-query-attention", "relation": "specializes", "target": "mlsys.attention", "statement": "is a kind of", "conditions": "Multiple query heads share each key-value head." }, { "source": "mlsys.grouped-query-attention", "relation": "reduces", "target": "mlsys.tpot", "statement": "reduces", "conditions": "Decode is bound by reading the KV cache, so fewer key-value heads cut bytes read per token." }, { "source": "mlsys.context-extension", "relation": "increases", "target": "mlsys.context", "statement": "extends the usable length of", "conditions": "The model is fine-tuned briefly on longer sequences after rescaling positions; quality at the new length must be checked." }, { "source": "mlsys.weight-serialization", "relation": "determines", "target": "mlsys.unsafe-deserialization", "statement": "determines exposure to", "conditions": "Executable formats such as pickle run code on load; data-only formats such as safetensors do not." }, { "source": "mlsys.weight-serialization", "relation": "determines", "target": "mlsys.cold-start", "statement": "sets weight-loading time in", "conditions": "Memory-mappable, zero-copy formats load faster than formats requiring full deserialization." }, { "source": "mlsys.model-conversion", "relation": "uses", "target": "mlsys.model-exchange-format", "statement": "commonly passes through a", "conditions": "Training and target runtimes differ and both support the exchange format's operator set." }, { "source": "mlsys.model-export", "relation": "produces", "target": "mlsys.model-artifact", "statement": "produces a deployable", "conditions": "Operators, shapes, and weights are fixed so the artifact runs without the original program." }, { "source": "mlsys.model-export", "relation": "uses", "target": "mlsys.tracing-jit", "statement": "captures the graph with", "conditions": "The program is traced or captured on example inputs; untraced control flow is lost." }, { "source": "mlsys.model-signature", "relation": "part-of", "target": "mlsys.model-artifact", "statement": "is the interface declared in", "conditions": "The artifact records input and output names, types, and shapes." }, { "source": "mlsys.model-server", "relation": "requires", "target": "mlsys.model-signature", "statement": "validates requests against", "conditions": "The server decodes and checks requests using the artifact's declared inputs and outputs." }, { "source": "mlsys.vla-model", "relation": "specializes", "target": "mlsys.multimodal-model", "statement": "is a kind of", "conditions": "The model consumes images and language and emits actions through a shared backbone." }, { "source": "mlsys.multimodal-model", "relation": "uses", "target": "mlsys.transformer", "statement": "commonly uses as its shared backbone a", "conditions": "Encoded modality tokens are processed together with text tokens by a transformer." }, { "source": "mlsys.encoder-disaggregation", "relation": "uses", "target": "mlsys.modality-encoder", "statement": "schedules separately the", "conditions": "Image or audio encoding is heavy enough to stall token generation if co-scheduled." }, { "source": "mlsys.context-compaction", "relation": "mitigates", "target": "mlsys.context-degradation", "statement": "mitigates", "conditions": "Older material is summarized so relevant information stays short and near the end of the window." }, { "source": "mlsys.reranking", "relation": "mitigates", "target": "mlsys.context-degradation", "statement": "mitigates position effects behind", "conditions": "Reranked evidence is placed where models use it best, near the start or end of the context." }, { "source": "mlsys.autodiff", "relation": "increases", "target": "mlsys.memory-footprint", "statement": "retains forward activations, increasing", "conditions": "Reverse-mode differentiation stores intermediates from the forward pass for use in the backward pass." }, { "source": "mlsys.tensor-layout", "relation": "determines", "target": "mlsys.memory-coalescing", "statement": "determines whether accesses achieve", "conditions": "Strides decide whether adjacent threads touch adjacent addresses." }, { "source": "mlsys.mixed-precision", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "reduces activation and gradient bytes in", "conditions": "Activations and gradients are stored in 16-bit formats while a master copy of weights stays in higher precision." }, { "source": "mlsys.mixed-precision", "relation": "uses", "target": "mlsys.matrix-unit", "statement": "gains throughput from", "conditions": "The matrix engine supports the reduced input formats with wider accumulation." }, { "source": "mlsys.computation-graph", "relation": "uses", "target": "mlsys.tensor", "statement": "passes between operations a", "conditions": "Edges carry tensors whose shapes and types the graph records." }, { "source": "mlsys.tensor", "relation": "uses", "target": "mlsys.precision", "statement": "carries an element type drawn from", "conditions": "Each tensor records a numerical format that kernels must support." }, { "source": "mlsys.compilation", "relation": "uses", "target": "mlsys.computation-graph", "statement": "takes as input a", "conditions": "The compiler needs a captured graph rather than one operation at a time." }, { "source": "mlsys.tracing-jit", "relation": "part-of", "target": "mlsys.eager-execution", "statement": "is an alternative to", "conditions": "Both are ways to run framework programs; eager keeps host flexibility while capture enables whole-graph optimization." }, { "source": "mlsys.eager-execution", "relation": "determines", "target": "mlsys.fusion", "statement": "forecloses cross-operation", "conditions": "Each operation is launched as the host issues it, so no compiler sees neighboring operations." }, { "source": "mlsys.io-aware-attention", "relation": "implements", "target": "mlsys.attention", "statement": "exactly computes", "conditions": "The result matches standard softmax attention up to floating-point reordering." }, { "source": "mlsys.io-aware-attention", "relation": "uses", "target": "mlsys.tiling", "statement": "is built on", "conditions": "Query, key, and value blocks are staged through on-chip memory with an online softmax." }, { "source": "mlsys.convolution-lowering", "relation": "increases", "target": "mlsys.memory-footprint", "statement": "can increase, through duplicated patches,", "conditions": "im2col-style lowering copies overlapping input patches into a larger matrix." }, { "source": "mlsys.kernel-library", "relation": "implements", "target": "mlsys.matrix-multiply", "statement": "provides tuned", "conditions": "Vendor libraries supply GEMM and convolution kernels selected per shape and precision." }, { "source": "mlsys.kernel-library", "relation": "alternative-to", "target": "mlsys.kernel-dsl", "statement": "is an alternative to", "conditions": "Both supply operator kernels; libraries cover common shapes, DSLs let engineers write fused or novel kernels." }, { "source": "mlsys.compilation", "relation": "uses", "target": "mlsys.intermediate-representation", "statement": "lowers through", "conditions": "Graph, tensor, and loop-level representations carry the program between passes." }, { "source": "mlsys.sharding-propagation", "relation": "uses", "target": "mlsys.intermediate-representation", "statement": "propagates layouts through", "conditions": "Sharding annotations are attached to operations in the compiler IR." }, { "source": "mlsys.autotuning", "relation": "uses", "target": "mlsys.profiling", "statement": "measures candidates with", "conditions": "Candidate schedules are compiled and timed on the target, or a cost model is trained from such 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"target": "mlsys.caching-allocator", "statement": "is an alternative to", "conditions": "Both manage device buffers; static planning needs known lifetimes, caching allocation handles dynamic shapes." }, { "source": "mlsys.device-streams", "relation": "reduces", "target": "mlsys.dispatch", "statement": "hides, through asynchronous enqueue, exposed", "conditions": "The host can run ahead of the device, so launch work overlaps device execution until queues drain." }, { "source": "mlsys.graph-replay", "relation": "uses", "target": "mlsys.device-streams", "statement": "is captured from and replayed on", "conditions": "Work enqueued on a stream is recorded as a graph and replayed as one launch." }, { "source": "mlsys.caching-allocator", "relation": "reduces", "target": "mlsys.dispatch", "statement": "avoids synchronizing allocation calls in", "conditions": "Freed blocks are reused instead of calling the device allocator, which can synchronize the host." }, { "source": "mlsys.graph-replay", "relation": "reduces", "target": "mlsys.tpot", "statement": "reduces per-step launch overhead in", "conditions": "Decode steps launch many small kernels with static shapes, so host overhead is a large share of step time." }, { "source": "mlsys.loss-scaling", "relation": "increases", "target": "mlsys.numerical-stability", "statement": "keeps small gradients representable, improving", "conditions": "Reduced-precision gradient formats would otherwise underflow; overflow is detected and the step skipped." }, { "source": "mlsys.block-scaled-format", "relation": "mitigates", "target": "mlsys.activation-outliers", "statement": "limits the range damage from", "conditions": "A shared scale per small block confines an outlier's effect to its own block." }, { "source": "mlsys.quantization", "relation": "uses", "target": "mlsys.block-scaled-format", "statement": "can encode values in", "conditions": "Hardware or kernels support the block-scaled element and scale formats." }, { "source": "mlsys.matrix-unit", "relation": "determines", "target": "mlsys.accumulation-precision", "statement": "fixes the available", "conditions": "The matrix engine implements specific accumulation formats for each input format." }, { "source": "mlsys.function-transformations", "relation": "uses", "target": "mlsys.compilation", "statement": "exposes as a transform", "conditions": "Pure array functions are traced and handed to an XLA-style compiler." }, { "source": "mlsys.single-vs-multi-controller", "relation": "determines", "target": "mlsys.dispatch", "statement": "sets where per-step dispatch cost falls in", "conditions": "One controller issuing work to many devices must keep dispatch asynchronous and off the critical path." }, { "source": "mlsys.thread-hierarchy", "relation": "uses", "target": "mlsys.on-chip-sram", "statement": "shares within a thread block", "conditions": "Threads of a block cooperate through shared memory sized per block." }, { "source": "mlsys.thread-hierarchy", "relation": "determines", "target": "mlsys.memory-coalescing", "statement": "determines, through thread-to-address mapping,", "conditions": "Adjacent threads of a warp should access adjacent addresses." }, { "source": "mlsys.on-chip-sram", "relation": "limits", "target": "mlsys.occupancy", "statement": "bounds", "conditions": "Shared memory and registers per block limit how many warps can be resident." }, { "source": "mlsys.thread-hierarchy", "relation": "determines", "target": "mlsys.occupancy", "statement": "sets, through block size and per-thread resources,", "conditions": "Block shape and register or shared-memory use determine resident warps per multiprocessor." }, { "source": "mlsys.async-data-movement", "relation": "uses", "target": "mlsys.on-chip-sram", "statement": "stages operands into", "conditions": "Bulk copies land in shared memory while compute proceeds on the previous buffer." }, { "source": "mlsys.async-data-movement", "relation": "increases", "target": "mlsys.utilization", "statement": "raises matrix-engine", "conditions": "The kernel would otherwise stall its matrix units waiting on loads." }, { "source": "mlsys.matrix-engine-programming", "relation": "uses", "target": "mlsys.matrix-unit", "statement": "issues instructions to", "conditions": "Fragments are loaded in the shapes and layouts the unit accepts." }, { "source": "mlsys.matrix-engine-programming", "relation": "requires", "target": "mlsys.tensor-layout", "statement": "requires specific operand", "conditions": "Matrix instructions accept only particular fragment layouts, so data may need rearrangement." }, { "source": "mlsys.batch-invariance", "relation": "enables", "target": "mlsys.experiment-reproducibility", "statement": "enables bitwise-reproducible serving within", "conditions": "Kernels use fixed reduction strategies independent of batch size and co-batched requests." }, { "source": "mlsys.systolic-array", "relation": "specializes", "target": "mlsys.accelerator-execution-architecture", "statement": "is a kind of", "conditions": "Processing elements pass operands to neighbors under a fixed schedule." }, { "source": "mlsys.simt", "relation": "specializes", "target": "mlsys.accelerator-execution-architecture", "statement": "is a kind of", "conditions": "Thread groups issue the same instruction and divergent paths serialize." }, { "source": "mlsys.matrix-unit", "relation": "part-of", "target": "mlsys.accelerator-execution-architecture", "statement": "is a functional unit within", "conditions": "The accelerator pairs matrix engines with vector or scalar units." }, { "source": "mlsys.hbm", "relation": "part-of", "target": "mlsys.memory-hierarchy", "statement": "is the device-memory level of", "conditions": "The accelerator uses stacked DRAM as off-chip device memory." }, { "source": "mlsys.on-chip-sram", "relation": "part-of", "target": "mlsys.memory-hierarchy", "statement": "is the fastest managed level of", "conditions": "The 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load leaves circuits below the voltage they need for correct timing." }, { "source": "mlsys.systolic-array", "relation": "reduces", "target": "mlsys.data-movement-energy", "statement": "reduces", "conditions": "Operands are reused across processing elements instead of being refetched from register files or memory." }, { "source": "mlsys.dataflow", "relation": "determines", "target": "mlsys.locality", "statement": "determines", "conditions": "Local storage cannot hold every operand, so the schedule chooses which data is reused in place." }, { "source": "mlsys.systolic-array", "relation": "uses", "target": "mlsys.dataflow", "statement": "operates under a chosen", "conditions": "The array keeps one operand stationary, such as weights, while others stream." }, { "source": "mlsys.domain-specific-architecture", "relation": "increases", "target": "mlsys.energy-efficiency", "statement": "raises", "conditions": "Target operations dominate the workload and are supported by the specialized 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"relation": "specializes", "target": "mlsys.host-device-interconnect", "statement": "is a kind of", "conditions": "The link adds hardware coherence and load-store memory semantics over a host-device physical layer." }, { "source": "mlsys.coherent-interconnect", "relation": "enables", "target": "mlsys.kv-cache-offload", "statement": "can provide a pooled tier for", "conditions": "Attached or pooled memory is cheaper than device memory and its higher latency can be overlapped with compute." }, { "source": "mlsys.kv-cache-transfer", "relation": "uses", "target": "mlsys.peer-to-peer-transfer", "statement": "moves state with", "conditions": "Direct device or NIC transfers avoid staging KV blocks through host memory." }, { "source": "mlsys.rdma", "relation": "enables", "target": "mlsys.peer-to-peer-transfer", "statement": "can read and write device memory through", "conditions": "The NIC and driver support direct access to accelerator memory." }, { "source": "mlsys.tdp", "relation": "determines", "target": "mlsys.rack-power-density", "statement": "sets, with accelerators per rack,", "conditions": "Racks are provisioned for the sustained power of the chips they hold." }, { "source": "mlsys.tdp", "relation": "determines", "target": "mlsys.dark-silicon", "statement": "sets the fraction of", "conditions": "Transistor counts grow while the power envelope does not." }, { "source": "mlsys.power-capping", "relation": "uses", "target": "mlsys.dvfs", "statement": "enforces caps through", "conditions": "The device lowers clocks and voltage when measured power exceeds the cap." }, { "source": "mlsys.thermal-dynamics", "relation": "uses", "target": "mlsys.dvfs", "statement": "throttles through", "conditions": "Thermal controls lower frequency and voltage when temperature limits are approached." }, { "source": "mlsys.microcontroller", "relation": "uses", "target": "mlsys.on-device-runtime", "statement": "runs models through a lightweight", "conditions": "No operating system or 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"mlsys.hybrid-parallel", "statement": "is one dimension of", "conditions": "Pipeline stages are combined with data and tensor parallelism across nodes." }, { "source": "mlsys.state-sharding", "relation": "uses", "target": "mlsys.collectives", "statement": "gathers and scatters state through", "conditions": "Sharded parameters are all-gathered before use and gradients reduce-scattered after." }, { "source": "mlsys.state-sharding", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "divides per-worker", "conditions": "Parameters, gradients, or optimizer state are partitioned instead of replicated." }, { "source": "mlsys.fat-tree", "relation": "specializes", "target": "mlsys.network-topology", "statement": "is a kind of", "conditions": "The fabric is built from tiers of commodity switches." }, { "source": "mlsys.rail-optimized", "relation": "specializes", "target": "mlsys.network-topology", "statement": "is a kind of", "conditions": "Same-index accelerators across 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"mlsys.pipeline-bubble", "relation": "reduces", "target": "mlsys.model-flops-utilization", "statement": "lowers", "conditions": "Devices idle during fill and drain while their peak FLOPs still count in the denominator." }, { "source": "mlsys.tree-allreduce", "relation": "implements", "target": "mlsys.collectives", "statement": "is an algorithm for", "conditions": "Participants are arranged in trees or hierarchical groups." }, { "source": "mlsys.all-to-all", "relation": "specializes", "target": "mlsys.collectives", "statement": "is a kind of", "conditions": "Every participant sends distinct data to every other." }, { "source": "mlsys.alpha-beta-model", "relation": "measures", "target": "mlsys.tree-allreduce", "statement": "predicts the cost of", "conditions": "Link latency and bandwidth are roughly uniform and contention is ignored." }, { "source": "mlsys.gradient-compression", "relation": "reduces", "target": "mlsys.time-to-train", "statement": "can reduce", "conditions": "Training is 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"relation": "uses", "target": "mlsys.secure-aggregation", "statement": "can combine updates through", "conditions": "The server must not see individual client updates." }, { "source": "mlsys.client-sampling", "relation": "part-of", "target": "mlsys.federated-averaging", "statement": "is the per-round selection step of", "conditions": "Each round samples a subset of available clients." }, { "source": "mlsys.non-iid-data", "relation": "specializes", "target": "mlsys.distribution-shift", "statement": "is a cross-client form of", "conditions": "Clients' data-generating distributions differ from each other and from the population." }, { "source": "mlsys.federation-setting", "relation": "determines", "target": "mlsys.device-heterogeneity", "statement": "determines exposure to", "conditions": "Cross-device federations span varied hardware and runtimes; cross-silo ones are few and uniform." }, { "source": "mlsys.federation-setting", "relation": "determines", "target": "mlsys.failure-model", 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"mlsys.analytical-performance-model", "statement": "ranks plans with", "conditions": "The search evaluates candidate partitionings with a cost model of compute and communication." }, { "source": "mlsys.network-topology", "relation": "determines", "target": "mlsys.device-mesh", "statement": "determines the axis alignment of", "conditions": "The busiest parallel axis is mapped onto the fastest links." }, { "source": "mlsys.hybrid-parallel", "relation": "uses", "target": "mlsys.device-mesh", "statement": "assigns parallel dimensions to the axes of", "conditions": "Each parallelism dimension corresponds to a named mesh axis." }, { "source": "mlsys.pue", "relation": "measures", "target": "mlsys.warehouse-scale-computer", "statement": "measures facility overhead of", "conditions": "IT and total facility energy are metered over the same period." }, { "source": "mlsys.liquid-cooling", "relation": "reduces", "target": "mlsys.pue", "statement": "can reduce", "conditions": "Cooling energy is a large share of facility overhead and liquid loops need less fan and chiller work." }, { "source": "mlsys.liquid-cooling", "relation": "mitigates", "target": "mlsys.thermal-dynamics", "statement": "mitigates throttling in", "conditions": "Air cooling cannot hold chips below their thermal limits at the target power." }, { "source": "mlsys.power-oversubscription", "relation": "reduces", "target": "mlsys.tco", "statement": "reduces", "conditions": "More servers share provisioned power capacity because their peaks rarely coincide." }, { "source": "mlsys.warehouse-scale-computer", "relation": "uses", "target": "mlsys.power-oversubscription", "statement": "provisions power through", "conditions": "Fleet-wide power measurements show aggregate peaks below summed nameplate." }, { "source": "mlsys.warehouse-scale-computer", "relation": "determines", "target": "mlsys.tco", "statement": "is designed to minimize", "conditions": "Facility, hardware, and software choices are evaluated as one cost model." }, { "source": "mlsys.lossless-fabric", "relation": "requires", "target": "mlsys.congestion-control", "statement": "requires", "conditions": "Priority flow control alone spreads congestion and pauses; end-to-end rate control keeps queues short." }, { "source": "mlsys.lossless-fabric", "relation": "increases", "target": "mlsys.tail-latency", "statement": "can inflate", "conditions": "Pause frames cause head-of-line blocking that delays unrelated flows under congestion." }, { "source": "mlsys.congestion-control", "relation": "mitigates", "target": "mlsys.performance-interference", "statement": "mitigates network", "conditions": "Jobs share switch queues on a common fabric." }, { "source": "mlsys.rail-optimized", "relation": "uses", "target": "mlsys.scale-up-interconnect", "statement": "relies for cross-rail traffic on", "conditions": "Traffic between different rails is forwarded through the node's scale-up domain." }, { "source": "mlsys.in-network-reduction", "relation": "reduces", "target": "mlsys.collectives", "statement": "reduces link traffic and latency of", "conditions": "Switches support aggregation for the data types and reduction operations used." }, { "source": "mlsys.in-network-reduction", "relation": "alternative-to", "target": "mlsys.ring-allreduce", "statement": "is an alternative to", "conditions": "Both perform all-reduce; switch aggregation shortens paths while rings need only end-host support." }, { "source": "mlsys.parallel-file-system", "relation": "alternative-to", "target": "mlsys.object-storage", "statement": "is an alternative to", "conditions": "Both serve shared training data and checkpoints; file systems offer lower latency, object stores cheaper scalable capacity." }, { "source": "mlsys.checkpoint-io", "relation": "uses", "target": "mlsys.parallel-file-system", "statement": "commonly writes to a", "conditions": "Sharded checkpoints are written concurrently by many ranks." }, { "source": "mlsys.object-storage", "relation": "determines", "target": "mlsys.dataset-storage-layout", "statement": "favors large sequential shards in", "conditions": "Per-request latency is high, so many small reads are slow." }, { "source": "mlsys.async-checkpointing", "relation": "implements", "target": "mlsys.checkpoint-io", "statement": "hides the training stall from", "conditions": "A fast snapshot to host memory lets persistence proceed in the background." }, { "source": "mlsys.checkpoint-io", "relation": "determines", "target": "mlsys.checkpoint-interval", "statement": "sets the write-cost input to", "conditions": "The optimal interval scales with the square root of checkpoint cost times MTBF." }, { "source": "mlsys.data-parallel", "relation": "requires", "target": "mlsys.gang-scheduling", "statement": "requires", "conditions": "Synchronous replicas cannot start or step until every worker holds its allocation." }, { "source": "mlsys.gang-scheduling", "relation": "increases", "target": "mlsys.queueing", "statement": "can increase job", "conditions": "Large jobs wait until enough resources are free at once." }, { "source": "mlsys.bin-packing", "relation": "increases", "target": "mlsys.utilization", "statement": "raises", "conditions": "Jobs can share nodes without violating resource or isolation constraints." }, { "source": "mlsys.bin-packing", "relation": "increases", "target": "mlsys.performance-interference", "statement": "can increase", "conditions": "Dense co-location puts jobs on shared caches, memory bandwidth, and links." }, { "source": "mlsys.multi-tenancy", "relation": "uses", "target": "mlsys.gpu-partitioning", "statement": "can share devices through", "conditions": "Tenant workloads are too small to saturate whole accelerators." }, { "source": "mlsys.resource-quota", "relation": "part-of", "target": "mlsys.multi-tenancy", "statement": "is a policy within", "conditions": "Tenants share a cluster under stated limits and guarantees." }, { "source": "mlsys.resource-quota", "relation": "alternative-to", "target": "mlsys.dominant-resource-fairness", "statement": "is an alternative to", "conditions": "Both divide shared capacity among tenants; quotas are static limits, DRF adapts shares to demand." }, { "source": "mlsys.gpu-partitioning", "relation": "mitigates", "target": "mlsys.performance-interference", "statement": "mitigates, with hardware partitions,", "conditions": "Partitions give each workload dedicated memory and compute slices; time-slicing does not." }, { "source": "mlsys.elastic-training", "relation": "enables", "target": "mlsys.spot-capacity", "statement": "makes usable", "conditions": "Training continues while preempted workers leave and replacements join." }, { "source": "mlsys.ridge-point", "relation": "part-of", "target": "mlsys.roofline", "statement": "is the corner of", "conditions": "Both ceilings refer to the same device and memory level." }, { "source": "mlsys.memory-wall", "relation": "increases", "target": "mlsys.ridge-point", "statement": "moves to higher intensity the", "conditions": "Peak compute grows faster than bandwidth across hardware generations." }, { "source": "mlsys.parallel-efficiency", "relation": "measures", "target": "mlsys.strong-scaling", "statement": "measures", "conditions": "Problem size is fixed while resources grow." }, { "source": "mlsys.parallel-efficiency", "relation": "measures", "target": "mlsys.weak-scaling", "statement": "measures", "conditions": "Work per resource is fixed while resources and problem size grow." }, { "source": "mlsys.weak-scaling", "relation": "alternative-to", "target": "mlsys.strong-scaling", "statement": "is an alternative to", "conditions": "Both describe how to use added resources; one grows the problem, the other fixes it." }, { "source": "mlsys.critical-batch-size", "relation": "limits", "target": "mlsys.weak-scaling", "statement": "bounds useful data-parallel", "conditions": "Growing the global batch with worker count stops reducing optimization steps beyond the critical batch size." }, { "source": "mlsys.capacity-planning", "relation": "uses", "target": "mlsys.littles-law", "statement": "sizes concurrency with", "conditions": "Arrival rate and time in system are measured for a stable operating point." }, { "source": "mlsys.batching", "relation": "determines", "target": "mlsys.latency-throughput-curve", "statement": "moves the operating point along", "conditions": "Larger batches raise throughput while adding batch-formation and execution delay." }, { "source": "mlsys.queueing", "relation": "determines", "target": "mlsys.latency-throughput-curve", "statement": "bends upward the", "conditions": "Utilization approaches service capacity, so waiting time grows sharply." }, { "source": "mlsys.arrival-burstiness", "relation": "determines", "target": "mlsys.capacity-planning", "statement": "raises headroom required by", "conditions": "Capacity is provisioned for bursts at the target latency percentile, not mean load." }, { "source": "mlsys.model-fidelity", "relation": "measures", "target": "mlsys.performance-simulation", "statement": "bounds trust in", "conditions": "Simulator predictions are validated against measurement on reference workloads." }, { "source": "mlsys.model-fidelity", "relation": "requires", "target": "mlsys.profiling", "statement": "requires measurements from", "conditions": "Validation compares predictions with profiled or benchmarked execution." }, { "source": "mlsys.amdahls-law", "relation": "limits", "target": "mlsys.parallel-efficiency", "statement": "bounds", "conditions": "The unparallelized fraction is fixed while workers are added to a fixed-size problem." }, { "source": "mlsys.dataset-storage-layout", "relation": "determines", "target": "mlsys.input-pipeline", "statement": "sets the read and decode cost of", "conditions": "Record grouping, compression, and columnar layout decide how many bytes each batch must fetch and decode." }, { "source": "mlsys.dark-silicon", "relation": "limits", "target": "mlsys.compute-throughput", "statement": "bounds sustained all-unit", "conditions": "Power and thermal limits prevent every unit from running at full frequency at once." }, { "source": "mlsys.fat-tree", "relation": "reduces", "target": "mlsys.tco", "statement": "lowers network cost within", "conditions": "Identical commodity switches replace large high-radix core switches at equal bisection bandwidth." }, { "source": "mlsys.lineage", "relation": "detects", "target": "mlsys.data-dependency", "statement": "reveals undeclared and underused", "conditions": "Lineage must be captured at the feature or column level and compared against what the model actually consumes." }, { "source": "mlsys.data-contract", "relation": "mitigates", "target": "mlsys.data-dependency", "statement": "makes explicit and versions", "conditions": "Holds when every model input is consumed through a declared, owned contract rather than an ad hoc signal." }, { "source": "mlsys.sliced-evaluation", "relation": "detects", "target": "mlsys.entanglement", "statement": "exposes subset regressions caused by", "conditions": "Applies when a change to one input or hyperparameter is evaluated on the same fixed slices before and after." }, { "source": "mlsys.configuration-management", "relation": "enables", "target": "mlsys.experiment-reproducibility", "statement": "pins the settings required for", "conditions": "Holds when every run records the exact reviewed configuration version it executed." }, { "source": "mlsys.configuration-management", "relation": "enables", "target": "mlsys.rollback", "statement": "makes configuration changes revertible through", "conditions": "Applies when configuration is versioned and deployed through the same pipeline as code." }, { "source": "mlsys.feature-store", "relation": "part-of", "target": "mlsys.ml-platform", "statement": "is a shared component of", "conditions": "Typical when an organization centralizes feature definitions for many teams." }, { "source": "mlsys.model-registry", "relation": "part-of", "target": "mlsys.ml-platform", "statement": "is a shared component of", "conditions": "Typical when promotion and deployment are standardized across teams." }, { "source": "mlsys.infrastructure-as-code", "relation": "enables", "target": "mlsys.ml-platform", "statement": "provisions repeatable environments for", "conditions": "Holds when platform compute, storage, and services are declared in versioned files." }, { "source": "mlsys.ml-roles", "relation": "determines", "target": "mlsys.incident-response", "statement": "assigns on-call ownership in", "conditions": "Applies when data, model, and platform owners are named for each production model." }, { "source": "mlsys.undeclared-consumers", "relation": "increases", "target": "mlsys.technical-debt", "statement": "add hidden coupling to", "conditions": "Holds whenever model outputs are consumed without access control or registration." }, { "source": "mlsys.authorization", "relation": "mitigates", "target": "mlsys.undeclared-consumers", "statement": "prevents unregistered", "conditions": "Applies when prediction endpoints and output tables require granted access per consumer." }, { "source": "mlsys.production-readiness", "relation": "measures", "target": "mlsys.technical-debt", "statement": "scores accumulated", "conditions": "The rubric must be applied repeatedly to the same system to track change over time." }, { "source": "mlsys.production-readiness", "relation": "requires", "target": "mlsys.integration-testing", "statement": "requires evidence from", "conditions": "Holds for the infrastructure and pipeline sections of the rubric." }, { "source": "mlsys.supervised-fine-tuning", "relation": "specializes", "target": "mlsys.supervised-learning", "statement": "is a kind of", "conditions": "Holds when training uses input-output demonstrations as targets." }, { "source": "mlsys.supervised-fine-tuning", "relation": "implements", "target": "mlsys.transfer-learning", "statement": "implements", "conditions": "Applies when a pretrained model is adapted to a new task or behavior." }, { "source": "mlsys.rlhf", "relation": "requires", "target": "mlsys.supervised-fine-tuning", "statement": "starts from a policy produced by", "conditions": "Holds for the standard InstructGPT-style pipeline; some recipes skip or shorten the SFT stage." }, { "source": "mlsys.synthetic-data", "relation": "enables", "target": "mlsys.supervised-fine-tuning", "statement": "supplies instruction data for", "conditions": "Applies when generated demonstrations are filtered for quality and diversity." }, { "source": "mlsys.incremental-training", "relation": "causes", "target": "mlsys.catastrophic-forgetting", "statement": "can cause", "conditions": "Holds when new data differs from the original distribution and old data is not replayed." }, { "source": "mlsys.incremental-training", "relation": "uses", "target": "mlsys.experience-replay", "statement": "commonly mixes in", "conditions": "Applies when earlier data is retained and permitted for reuse." }, { "source": "mlsys.data-retention", "relation": "limits", "target": "mlsys.experience-replay", "statement": "limits which past examples are available to", "conditions": "Holds when retention or deletion policies remove raw training data after a fixed period." }, { "source": "mlsys.process-reward-model", "relation": "specializes", "target": "mlsys.reward-model", "statement": "is a step-level kind of", "conditions": "Holds when the scorer is trained to rate intermediate reasoning steps." }, { "source": "mlsys.rlvr", "relation": "specializes", "target": "mlsys.reinforcement-learning", "statement": "is a kind of", "conditions": "Holds when rewards come from programmatic outcome checks." }, { "source": "mlsys.rlvr", "relation": "requires", "target": "mlsys.rl-rollout", "statement": "requires many sampled responses from", "conditions": "Holds for policy-gradient training where rewards are computed on generated outputs." }, { "source": "mlsys.verifier", "relation": "causes", "target": "mlsys.reward-hacking", "statement": "imperfect checks can cause", "conditions": "Holds when a policy or search is optimized against a verifier with exploitable false positives." }, { "source": "mlsys.best-of-n", "relation": "increases", "target": "mlsys.unit-cost", "statement": "multiplies generation cost in", "conditions": "Generation cost scales roughly linearly with n unless prefixes are shared." }, { "source": "mlsys.best-of-n", "relation": "alternative-to", "target": "mlsys.self-consistency", "statement": "is an alternative to", "conditions": "Both spend parallel samples; best-of-n needs a scorer while self-consistency needs answers that can be voted on." }, { "source": "mlsys.best-of-n", "relation": "requires", "target": "mlsys.sampling-controls", "statement": "requires candidate diversity from", "conditions": "Holds because greedy decoding would return n identical candidates." }, { "source": "mlsys.pass-at-k", "relation": "specializes", "target": "mlsys.metrics", "statement": "is a kind of", "conditions": "Applies when outputs have an automatic correctness check, as in code generation." }, { "source": "mlsys.sampling-controls", "relation": "determines", "target": "mlsys.pass-at-k", "statement": "temperature shapes", "conditions": "The best temperature rises with k because more diverse samples help coverage." }, { "source": "mlsys.inference-time-search", "relation": "increases", "target": "mlsys.unit-cost", "statement": "multiplies per-query compute in", "conditions": "Holds in proportion to the number of expanded and scored nodes." }, { "source": "mlsys.inference-time-search", "relation": "uses", "target": "mlsys.prefix-cache", "statement": "shares partial-solution state through", "conditions": "Applies when branches share prompt and partial-solution prefixes on one server." }, { "source": "mlsys.self-consistency", "relation": "increases", "target": "mlsys.unit-cost", "statement": "multiplies generation cost in", "conditions": "Cost scales with the number of sampled reasoning paths." }, { "source": "mlsys.test-time-scaling", "relation": "uses", "target": "mlsys.verifier", "statement": "uses search guided by", "conditions": "Applies to the search-based way of spending inference compute, as opposed to sequential revision." }, { "source": "mlsys.pairwise-preference-eval", "relation": "uses", "target": "mlsys.llm-judge", "statement": "can use as the judge", "conditions": "Applies when pairwise comparisons are graded by a model rather than people." }, { "source": "mlsys.pairwise-preference-eval", "relation": "uses", "target": "mlsys.human-evaluation", "statement": "aggregates votes from", "conditions": "Holds for crowd-sourced arenas where people choose between two outputs." }, { "source": "mlsys.annotator-agreement", "relation": "measures", "target": "mlsys.human-evaluation", "statement": "measures reliability of", "conditions": "Requires several raters to judge overlapping items." }, { "source": "mlsys.evaluation-harness", "relation": "uses", "target": "mlsys.llm-judge", "statement": "can score open-ended outputs with", "conditions": "Applies when tasks lack exact-match references." }, { "source": "mlsys.llm-judge", "relation": "detects", "target": "mlsys.hallucination", "statement": "can flag", "conditions": "Holds when the judge sees the source evidence and its error rate has been measured against people." }, { "source": "mlsys.missing-data", "relation": "causes", "target": "mlsys.train-serve-skew", "statement": "can cause", "conditions": "Holds when missingness patterns or imputation differ between offline data and live requests." }, { "source": "mlsys.data-validation", "relation": "detects", "target": "mlsys.missing-data", "statement": "flags unexpected", "conditions": "Requires declared expectations on completeness per feature." }, { "source": "mlsys.data-validation", "relation": "measures", "target": "mlsys.data-quality", "statement": "checks", "conditions": "Covers only the properties the checks encode, not semantic correctness." }, { "source": "mlsys.data-validation", "relation": "detects", "target": "mlsys.schema-evolution", "statement": "flags undeclared", "conditions": "Holds when incoming data is compared against the expected schema version." }, { "source": "mlsys.schema-evolution", "relation": "causes", "target": "mlsys.train-serve-skew", "statement": "uncoordinated changes can cause", "conditions": "Holds when training and serving read different schema versions or interpret a changed field differently." }, { "source": "mlsys.evaluation-split", "relation": "mitigates", "target": "mlsys.leakage", "statement": "guards against", "conditions": "Holds when splits respect time, entity, and duplicate boundaries." }, { "source": "mlsys.evaluation-split", "relation": "mitigates", "target": "mlsys.selection-bias", "statement": "keeps a final test partition free of", "conditions": "Holds only if the final test partition is not used for any selection decision." }, { "source": "mlsys.data-mixture", "relation": "specializes", "target": "mlsys.data-selection", "statement": "is domain-level", "conditions": "Applies when sampling weights are set per source rather than per example." }, { "source": "mlsys.active-learning", "relation": "specializes", "target": "mlsys.data-selection", "statement": "is label-acquisition", "conditions": "Applies when the cost being allocated is annotation effort." }, { "source": "mlsys.weak-supervision", "relation": "alternative-to", "target": "mlsys.active-learning", "statement": "is an alternative to", "conditions": "Both reduce manual labeling cost; one writes labeling heuristics, the other chooses what humans label." }, { "source": "mlsys.quality-filtering", "relation": "increases", "target": "mlsys.data-quality", "statement": "raises average", "conditions": "Holds when the filter's notion of quality matches the target task; filters can also remove useful diversity." }, { "source": "mlsys.quality-filtering", "relation": "uses", "target": "mlsys.batch-data-processing", "statement": "runs at corpus scale on", "conditions": "Applies to web-scale pretraining corpora." }, { "source": "mlsys.data-augmentation", "relation": "specializes", "target": "mlsys.regularization", "statement": "acts as a form of", "conditions": "Holds when transformations are label-preserving." }, { "source": "mlsys.data-augmentation", "relation": "mitigates", "target": "mlsys.distribution-shift", "statement": "can reduce sensitivity to", "conditions": "Holds only for shifts that the chosen transformations actually model." }, { "source": "mlsys.synthetic-data", "relation": "alternative-to", "target": "mlsys.data-augmentation", "statement": "is an alternative to", "conditions": "Both enlarge training data; augmentation transforms real examples while synthetic data generates new ones." }, { "source": "mlsys.domain-randomization", "relation": "uses", "target": "mlsys.synthetic-data", "statement": "generates varied", "conditions": "Applies to simulator-generated training data for transfer to the real world." }, { "source": "mlsys.data-acquisition", "relation": "causes", "target": "mlsys.distribution-shift", "statement": "sampling bias can cause", "conditions": "Holds when the collected population differs from the deployment population." }, { "source": "mlsys.stream-processing", "relation": "alternative-to", "target": "mlsys.batch-data-processing", "statement": "is an alternative to", "conditions": "Choose by whether the data is unbounded and how fresh outputs must be." }, { "source": "mlsys.online-learning", "relation": "uses", "target": "mlsys.stream-processing", "statement": "consumes examples from", "conditions": "Applies when updates happen as events arrive." }, { "source": "mlsys.online-learning", "relation": "mitigates", "target": "mlsys.concept-drift", "statement": "adapts to", "conditions": "Holds when labels arrive quickly enough to track the change." }, { "source": "mlsys.feature-backfill", "relation": "mitigates", "target": "mlsys.train-serve-skew", "statement": "realigns history to reduce", "conditions": "Holds when the backfill uses the same definition that serving computes." }, { "source": "mlsys.data-versioning", "relation": "part-of", "target": "mlsys.artifact-version", "statement": "is part of", "conditions": "Holds when a release records the exact dataset versions it was trained and evaluated on." }, { "source": "mlsys.lineage", "relation": "requires", "target": "mlsys.data-versioning", "statement": "references immutable", "conditions": "Lineage is only replayable if the referenced datasets are versioned." }, { "source": "mlsys.batch-data-processing", "relation": "uses", "target": "mlsys.dataset-storage-layout", "statement": "prunes reads through", "conditions": "Holds for columnar, partitioned layouts with statistics." }, { "source": "mlsys.backpressure", "relation": "limits", "target": "mlsys.queueing", "statement": "bounds queue growth and", "conditions": "Holds when upstream producers honor the signal rather than buffering unboundedly." }, { "source": "mlsys.regularization", "relation": "mitigates", "target": "mlsys.membership-inference", "statement": "reduces the overfitting exploited by", "conditions": "Reduces but does not bound leakage; formal bounds need differential privacy." }, { "source": "mlsys.critical-batch-size", "relation": "limits", "target": "mlsys.batch-size", "statement": "bounds the useful", "conditions": "Holds for a given target loss and training phase; it grows as loss falls." }, { "source": "mlsys.activation-recompute", "relation": "increases", "target": "mlsys.execution-time", "statement": "adds about one extra forward pass to", "conditions": "Holds for full recomputation; selective schemes recompute less." }, { "source": "mlsys.activation-recompute", "relation": "alternative-to", "target": "mlsys.memory-offloading", "statement": "is an alternative to", "conditions": "Both relieve accelerator memory; one spends compute, the other interconnect bandwidth." }, { "source": "mlsys.transfer-learning", "relation": "reduces", "target": "mlsys.training-compute-estimate", "statement": "reduces target-task", "conditions": "Holds when a suitable pretrained model exists for the target domain." }, { "source": "mlsys.self-supervised-learning", "relation": "enables", "target": "mlsys.transfer-learning", "statement": "pretrains representations for", "conditions": "Holds when the pretraining data covers the target domain." }, { "source": "mlsys.self-supervised-learning", "relation": "produces", "target": "mlsys.embeddings", "statement": "produces", "conditions": "Applies to contrastive and masked-prediction objectives." }, { "source": "mlsys.slice-discovery", "relation": "uses", "target": "mlsys.unsupervised-learning", "statement": "clusters errors with", "conditions": "Applies to methods that group examples without predefined slice labels." }, { "source": "mlsys.slice-discovery", "relation": "uses", "target": "mlsys.embeddings", "statement": "searches for coherent slices in", "conditions": "Holds for embedding-based methods such as Domino." }, { "source": "mlsys.rlhf", "relation": "specializes", "target": "mlsys.reinforcement-learning", "statement": "is a kind of", "conditions": "Holds for PPO-style optimization against a learned reward." }, { "source": "mlsys.preference-optimization", "relation": "alternative-to", "target": "mlsys.rlhf", "statement": "is an alternative to", "conditions": "Both align to preference data; DPO skips the separate reward model and RL loop." }, { "source": "mlsys.preference-optimization", "relation": "reduces", "target": "mlsys.training-state", "statement": "avoids reward and value models in", "conditions": "Holds relative to PPO-based RLHF; a frozen reference model is still needed." }, { "source": "mlsys.reward-model", "relation": "requires", "target": "mlsys.labels", "statement": "requires reliable preference", "conditions": "Holds because reward accuracy is bounded by comparison label agreement." }, { "source": "mlsys.vla-model", "relation": "uses", "target": "mlsys.imitation-learning", "statement": "is commonly trained by", "conditions": "Applies when robot demonstrations supply the action targets." }, { "source": "mlsys.batch-size", "relation": "determines", "target": "mlsys.learning-rate-schedule", "statement": "shifts the best peak rate of", "conditions": "Holds below the critical batch size, where learning rate is typically scaled with batch." }, { "source": "mlsys.hyperparameter-transfer", "relation": "determines", "target": "mlsys.learning-rate-schedule", "statement": "carries tuned settings into", "conditions": "Holds under a parameterization such as muP that keeps optima stable across width." }, { "source": "mlsys.hyperparameter-transfer", "relation": "uses", "target": "mlsys.hyperparameter-search", "statement": "runs on small proxies", "conditions": "Holds when proxy models share the target's parameterization." }, { "source": "mlsys.hyperparameter-search", "relation": "uses", "target": "mlsys.experiment-tracking", "statement": "compares trials through", "conditions": "Applies whenever more than a handful of configurations are evaluated." }, { "source": "mlsys.gradient-accumulation", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "reduces per-step activation", "conditions": "Holds because activation memory scales with micro-batch, not effective batch." }, { "source": "mlsys.gradient-accumulation", "relation": "reduces", "target": "mlsys.collectives", "statement": "reduces the frequency of", "conditions": "Holds when gradient all-reduce is deferred to the last micro-batch." }, { "source": "mlsys.lora", "relation": "uses", "target": "mlsys.low-rank-factorization", "statement": "parameterizes updates by", "conditions": "Holds when the update rank is much smaller than the frozen weight dimensions." }, { "source": "mlsys.weight-sync", "relation": "uses", "target": "mlsys.collectives", "statement": "broadcasts weights through", "conditions": "Applies when training and rollout workers sit on separate device groups." }, { "source": "mlsys.weight-sync", "relation": "reduces", "target": "mlsys.training-goodput", "statement": "sync time reduces", "conditions": "Holds when transfers are not overlapped with rollout or training." }, { "source": "mlsys.rl-rollout", "relation": "increases", "target": "mlsys.policy-staleness", "statement": "asynchronous generation increases", "conditions": "Holds when rollouts overlap with policy updates." }, { "source": "mlsys.on-device-training", "relation": "uses", "target": "mlsys.peft", "statement": "updates sparse parameter subsets via", "conditions": "Applies under tight device memory where full backpropagation does not fit." }, { "source": "mlsys.federated-learning", "relation": "requires", "target": "mlsys.on-device-training", "statement": "requires", "conditions": "Holds for cross-device federation; cross-silo clients train on servers." }, { "source": "mlsys.cross-validation", "relation": "specializes", "target": "mlsys.evaluation-split", "statement": "is a rotating", "conditions": "Applies when data is too scarce for a single fixed validation split and examples are exchangeable." }, { "source": "mlsys.conformal-prediction", "relation": "requires", "target": "mlsys.evaluation-split", "statement": "requires a held-out calibration", "conditions": "Coverage holds only under exchangeability with that calibration split." }, { "source": "mlsys.sequential-testing", "relation": "enables", "target": "mlsys.rollout", "statement": "enables early stopping of a harmful stage in", "conditions": "Holds when the test's error guarantees allow continuous monitoring." }, { "source": "mlsys.feedback-loop", "relation": "causes", "target": "mlsys.experiment-interference", "statement": "shared retraining data can cause", "conditions": "Holds when control and treatment models learn from logs that both arms influenced." }, { "source": "mlsys.benchmark-contamination", "relation": "increases", "target": "mlsys.metrics", "statement": "inflates", "conditions": "Holds when the evaluated items or close variants were seen in training." }, { "source": "mlsys.behavioral-testing", "relation": "uses", "target": "mlsys.metamorphic-testing", "statement": "builds invariance and directional tests from", "conditions": "Applies to CheckList-style INV and DIR tests." }, { "source": "mlsys.release-gate", "relation": "uses", "target": "mlsys.behavioral-testing", "statement": "can block on failures of", "conditions": "Applies when capability tests are part of the predeclared checks." }, { "source": "mlsys.release-gate", "relation": "uses", "target": "mlsys.dangerous-capability-eval", "statement": "can block on thresholds crossed in", "conditions": "Applies to frontier-model releases governed by a scaling policy." }, { "source": "mlsys.admission", "relation": "limits", "target": "mlsys.queueing", "statement": "bounds by shedding excess load", "conditions": "Holds when rejected work is not retried immediately into the same queue." }, { "source": "mlsys.admission", "relation": "implements", "target": "mlsys.graceful-degradation", "statement": "implements load shedding for", "conditions": "Holds when shedding low-priority work keeps the remaining requests within their SLO." }, { "source": "mlsys.autoscaling", "relation": "increases", "target": "mlsys.utilization", "statement": "raises average", "conditions": "Holds when demand varies and scale-down is permitted." }, { "source": "mlsys.paged-kv", "relation": "enables", "target": "mlsys.prefix-cache", "statement": "enables block sharing for", "conditions": "Holds when the allocator supports copy-on-write sharing of identical prefix blocks." }, { "source": "mlsys.paged-kv", "relation": "increases", "target": "mlsys.service-capacity", "statement": "raises batch size and so", "conditions": "Holds when KV memory fragmentation was limiting concurrency." }, { "source": "mlsys.speculative-decoding", "relation": "reduces", "target": "mlsys.tpot", "statement": "reduces", "conditions": "Holds when the draft acceptance rate is high and decode is memory-bound." }, { "source": "mlsys.speculative-decoding", "relation": "increases", "target": "mlsys.arithmetic-intensity", "statement": "verifies several tokens per weight read, raising", "conditions": "Holds at small batch sizes where decode is memory-bound." }, { "source": "mlsys.prefix-cache", "relation": "reduces", "target": "mlsys.ttft", "statement": "skips recomputed prefill to reduce", "conditions": "Holds when requests share long identical prefixes and the cache hits." }, { "source": "mlsys.queueing", "relation": "increases", "target": "mlsys.ttft", "statement": "increases", "conditions": "Holds under load when requests wait for a prefill slot." }, { "source": "mlsys.rag", "relation": "increases", "target": "mlsys.ttft", "statement": "lengthens prompts and so", "conditions": "Holds because retrieved passages add prefill tokens and retrieval adds a serial step." }, { "source": "mlsys.online-inference", "relation": "alternative-to", "target": "mlsys.batch-inference", "statement": "is an alternative to", "conditions": "Choose by required freshness, latency, and cost per prediction." }, { "source": "mlsys.online-inference", "relation": "uses", "target": "mlsys.model-server", "statement": "uses", "conditions": "Applies when predictions are served per request behind a network interface." }, { "source": "mlsys.batch-inference", "relation": "reduces", "target": "mlsys.unit-cost", "statement": "reduces", "conditions": "Holds when predictions can be precomputed for a known input set." }, { "source": "mlsys.model-server", "relation": "requires", "target": "mlsys.model-artifact", "statement": "loads and executes", "conditions": "Holds for any server that loads serialized weights and graph at startup or on version change." }, { "source": "mlsys.multi-model-serving", "relation": "increases", "target": "mlsys.utilization", "statement": "increases", "conditions": "Holds when individual models have low or bursty traffic." }, { "source": "mlsys.model-cascade", "relation": "alternative-to", "target": "mlsys.model-routing", "statement": "is an alternative to", "conditions": "Routing decides before calling a model; a cascade escalates after a cheap model's answer." }, { "source": "mlsys.hedged-requests", "relation": "increases", "target": "mlsys.arrival-rate", "statement": "adds duplicate load to", "conditions": "Extra load stays small when hedges fire only after a high-percentile delay." }, { "source": "mlsys.hedged-requests", "relation": "mitigates", "target": "mlsys.stragglers", "statement": "mitigates", "conditions": "Holds when slowness is replica-specific rather than shared." }, { "source": "mlsys.continuous-batching", "relation": "uses", "target": "mlsys.paged-kv", "statement": "relies on dynamic allocation from", "conditions": "Holds for vLLM-style engines where sequences join and leave each iteration." }, { "source": "mlsys.load-balancing", "relation": "reduces", "target": "mlsys.queueing", "statement": "reduces", "conditions": "Holds when load signals are fresh and replicas are interchangeable." }, { "source": "mlsys.load-balancing", "relation": "reduces", "target": "mlsys.tail-latency", "statement": "reduces", "conditions": "Holds when imbalance, not total capacity, causes the tail." }, { "source": "mlsys.chunked-prefill", "relation": "alternative-to", "target": "mlsys.pd-disaggregation", "statement": "is an alternative to", "conditions": "Both limit prefill-decode interference; one colocates with chunking, the other separates pools." }, { "source": "mlsys.chunked-prefill", "relation": "uses", "target": "mlsys.continuous-batching", "statement": "piggybacks prefill chunks on", "conditions": "Holds when the scheduler admits work at each iteration under a per-iteration token budget." }, { "source": "mlsys.constrained-decoding", "relation": "enables", "target": "mlsys.function-calling", "statement": "guarantees well-formed arguments for", "conditions": "Holds when the tool schema is compiled to a grammar." }, { "source": "mlsys.function-calling", "relation": "uses", "target": "mlsys.grammar", "statement": "constrains arguments with", "conditions": "Applies when tool arguments must match a declared schema." }, { "source": "mlsys.output-validation", "relation": "implements", "target": "mlsys.grammar", "statement": "checks after generation", "conditions": "Applies when outputs are parsed against the schema after generation instead of masked during it." }, { "source": "mlsys.output-validation", "relation": "increases", "target": "mlsys.tail-latency", "statement": "retries increase", "conditions": "Holds when invalid outputs trigger regeneration." }, { "source": "mlsys.reasoning-budget", "relation": "limits", "target": "mlsys.unit-cost", "statement": "caps the reasoning share of", "conditions": "Holds when reasoning tokens are billed or provisioned like output tokens." }, { "source": "mlsys.reasoning-budget", "relation": "limits", "target": "mlsys.kv-cache", "statement": "bounds growth of", "conditions": "Holds because reasoning tokens stay in context until the answer." }, { "source": "mlsys.dense-retrieval", "relation": "uses", "target": "mlsys.embeddings", "statement": "uses", "conditions": "Holds for bi-encoder retrieval where queries and records are embedded separately." }, { "source": "mlsys.dense-retrieval", "relation": "specializes", "target": "mlsys.retrieval", "statement": "is a kind of", "conditions": "Applies when relevance is scored by similarity of learned vectors." }, { "source": "mlsys.lexical-retrieval", "relation": "specializes", "target": "mlsys.retrieval", "statement": "is a kind of", "conditions": "Applies when relevance is scored by weighted term overlap over an inverted index." }, { "source": "mlsys.reranking", "relation": "part-of", "target": "mlsys.ranking-funnel", "statement": "is a late stage of", "conditions": "Holds when a cheap candidate stage precedes a costlier scoring model under one latency budget." }, { "source": "mlsys.reranking", "relation": "uses", "target": "mlsys.transformer", "statement": "commonly uses cross-encoder", "conditions": "Applies to neural rerankers that jointly encode query and candidate." }, { "source": "mlsys.on-device-runtime", "relation": "requires", "target": "mlsys.model-conversion", "statement": "requires", "conditions": "Holds because runtimes accept only their own formats and operator sets." }, { "source": "mlsys.on-device-runtime", "relation": "uses", "target": "mlsys.edge-npu", "statement": "delegates supported operators to", "conditions": "Applies when the device has an accelerator and a delegate for it." }, { "source": "mlsys.ota-model-update", "relation": "requires", "target": "mlsys.artifact-signing", "statement": "must verify", "conditions": "Holds for any fleet where tampered models could be pushed." }, { "source": "mlsys.ota-model-update", "relation": "uses", "target": "mlsys.rollback", "statement": "relies on on-device", "conditions": "Requires the previous model to stay on the device." }, { "source": "mlsys.device-cloud-partitioning", "relation": "reduces", "target": "mlsys.energy-per-inference", "statement": "can reduce on-device", "conditions": "Holds when uplink cost is lower than the compute it moves off device." }, { "source": "mlsys.device-heterogeneity", "relation": "determines", "target": "mlsys.device-cloud-partitioning", "statement": "shifts the best split in", "conditions": "Holds because device speed and network differ widely across the fleet." }, { "source": "mlsys.model-registry", "relation": "enables", "target": "mlsys.rollback", "statement": "keeps prior versions available for", "conditions": "Holds when prior versions and their serving configuration remain registered and deployable." }, { "source": "mlsys.shadow-deployment", "relation": "detects", "target": "mlsys.train-serve-skew", "statement": "can expose", "conditions": "Holds when shadow outputs are compared with offline predictions on the same inputs." }, { "source": "mlsys.shadow-deployment", "relation": "increases", "target": "mlsys.unit-cost", "statement": "duplicates inference cost in", "conditions": "Holds for the shadow period because every mirrored request runs on both models." }, { "source": "mlsys.ml-ci-cd", "relation": "uses", "target": "mlsys.integration-testing", "statement": "runs", "conditions": "Applies on each change to code, data checks, or model artifacts." }, { "source": "mlsys.ml-ci-cd", "relation": "part-of", "target": "mlsys.mlops-maturity", "statement": "marks higher levels of", "conditions": "Holds for maturity models in which automated pipeline delivery defines the top level." }, { "source": "mlsys.drift-detection", "relation": "part-of", "target": "mlsys.monitoring", "statement": "is part of", "conditions": "Applies when distribution checks run alongside service and quality signals in production." }, { "source": "mlsys.drift-detection", "relation": "requires", "target": "mlsys.inference-logging", "statement": "compares distributions from", "conditions": "Holds because production distributions are reconstructed from logged inputs and predictions." }, { "source": "mlsys.delayed-outcomes", "relation": "limits", "target": "mlsys.drift-detection", "statement": "limits outcome-based", "conditions": "Holds when labels arrive late; input-distribution checks remain available." }, { "source": "mlsys.alerting", "relation": "uses", "target": "mlsys.sli", "statement": "fires on thresholds over", "conditions": "Applies when alerts are defined over user-facing indicators rather than raw resource metrics." }, { "source": "mlsys.concept-drift", "relation": "specializes", "target": "mlsys.distribution-shift", "statement": "is the input-target form of", "conditions": "Holds when P(y|x) changes even if the input distribution does not." }, { "source": "mlsys.workflow-orchestration", "relation": "implements", "target": "mlsys.continuous-training", "statement": "runs the pipelines of", "conditions": "Holds when retraining, validation, and registration steps run as an orchestrated pipeline." }, { "source": "mlsys.postmortem", "relation": "uses", "target": "mlsys.failure-attribution", "statement": "uses", "conditions": "Applies to ML incidents whose cause could lie in data, model, code, or infrastructure." }, { "source": "mlsys.failure-attribution", "relation": "uses", "target": "mlsys.lineage", "statement": "traces data and artifact versions through", "conditions": "Holds when the failing prediction can be tied to the data and artifact versions that produced it." }, { "source": "mlsys.distributed-tracing", "relation": "enables", "target": "mlsys.failure-attribution", "statement": "localizes the component behind", "conditions": "Holds when every component a request traverses propagates the trace context." }, { "source": "mlsys.llm-call-tracing", "relation": "enables", "target": "mlsys.cost-attribution", "statement": "attributes token spend per step for", "conditions": "Holds when spans record token counts and model identity per call." }, { "source": "mlsys.model-deprecation", "relation": "requires", "target": "mlsys.lineage", "statement": "finds downstream consumers through", "conditions": "Holds when lineage records which downstream jobs and services read the model's outputs." }, { "source": "mlsys.artifact-archival", "relation": "part-of", "target": "mlsys.model-deprecation", "statement": "is the final stage of", "conditions": "Applies when a retired model must stay reproducible or auditable after removal from service." }, { "source": "mlsys.artifact-archival", "relation": "enables", "target": "mlsys.algorithmic-audit", "statement": "preserves evidence for", "conditions": "Holds when evaluation records and data references are kept for the audit horizon." }, { "source": "mlsys.kv-cache-offload", "relation": "specializes", "target": "mlsys.storage-tiering", "statement": "is a kind of", "conditions": "Applies when KV blocks move between accelerator memory, host memory, and SSD by reuse likelihood." }, { "source": "mlsys.kv-cache-transfer", "relation": "uses", "target": "mlsys.rdma", "statement": "uses", "conditions": "Applies in disaggregated clusters with RDMA fabrics." }, { "source": "mlsys.kv-cache-transfer", "relation": "uses", "target": "mlsys.communication-overlap", "statement": "hides latency with", "conditions": "Holds when layer-wise KV transfer can proceed while later layers still compute." }, { "source": "mlsys.lm-program-optimization", "relation": "requires", "target": "mlsys.metrics", "statement": "optimizes against an end-to-end", "conditions": "Holds because the optimizer can only improve what the declared metric scores." }, { "source": "mlsys.multi-adapter-serving", "relation": "reduces", "target": "mlsys.unit-cost", "statement": "reduces", "conditions": "Holds relative to one dedicated deployment per fine-tuned model." }, { "source": "mlsys.denoising-step-reduction", "relation": "uses", "target": "mlsys.distillation", "statement": "often uses step", "conditions": "Applies to progressive and consistency distillation, not to training-free fast samplers." }, { "source": "mlsys.denoising-step-reduction", "relation": "reduces", "target": "mlsys.execution-time", "statement": "reduces per-sample", "conditions": "Holds roughly in proportion to the number of network evaluations removed." }, { "source": "mlsys.diffusion-feature-caching", "relation": "increases", "target": "mlsys.memory-footprint", "statement": "increases cached-activation", "conditions": "Holds because cached features from earlier steps must stay resident." }, { "source": "mlsys.diffusion-feature-caching", "relation": "reduces", "target": "mlsys.execution-time", "statement": "reduces", "conditions": "Holds when adjacent-step features change little, so skipped blocks lose little quality." }, { "source": "mlsys.quantized-vector-index", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "compresses vectors to reduce", "conditions": "Holds with recall loss that grows as code size shrinks." }, { "source": "mlsys.filtered-vector-search", "relation": "uses", "target": "mlsys.graph-vector-index", "statement": "extends traversal of", "conditions": "Applies to predicate-agnostic methods that densify a proximity graph to keep filtered recall." }, { "source": "mlsys.index-freshness", "relation": "limits", "target": "mlsys.retrieval", "statement": "bounds recall on recently changed records in", "conditions": "Holds when inserts and deletes reach the index later than the source of truth." }, { "source": "mlsys.agent-evaluation", "relation": "uses", "target": "mlsys.llm-judge", "statement": "can grade trajectories with", "conditions": "Applies where task success lacks a programmatic check." }, { "source": "mlsys.deterministic-replay", "relation": "enables", "target": "mlsys.agent-evaluation", "statement": "makes repeatable", "conditions": "Holds when tool responses and model outputs are recorded so a run can be re-executed exactly." }, { "source": "mlsys.critical-batch-size", "relation": "limits", "target": "mlsys.time-to-train", "statement": "bounds the data-parallel speedup of", "conditions": "Holds because beyond it more examples per step no longer cut the steps needed to reach the target loss." }, { "source": "mlsys.distillation", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "reduces the serving memory footprint by producing a smaller student", "conditions": "When the student architecture is smaller than the teacher and reaches acceptable quality on the target metric." }, { "source": "mlsys.distillation", "relation": "enables", "target": "mlsys.speculative-decoding", "statement": "produces well-aligned draft models for", "conditions": "When the draft is distilled from the target model so its token distribution matches closely enough to raise acceptance rates." }, { "source": "mlsys.pruning", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "reduces", "conditions": "When the pruned weights are stored in a compressed or smaller dense form rather than as explicit zeros." }, { "source": "mlsys.structured-pruning", "relation": "reduces", "target": "mlsys.execution-time", "statement": "reduces dense-hardware", "conditions": "When whole channels, filters, or heads are removed so the remaining layers are smaller dense operations." }, { "source": "mlsys.low-rank-factorization", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "reduces", "conditions": "When the chosen rank is much smaller than the matrix dimensions and the factored model keeps acceptable accuracy." }, { "source": "mlsys.quantization-aware-training", "relation": "alternative-to", "target": "mlsys.post-training-quantization", "statement": "is an alternative to", "conditions": "QAT recovers more accuracy at low bit widths but needs a training run; PTQ needs only a calibration set." }, { "source": "mlsys.quantization-aware-training", "relation": "enables", "target": "mlsys.edge-npu", "statement": "enables integer-only execution on", "conditions": "When the simulated quantization matches the accelerator's integer arithmetic and requantization rules." }, { "source": "mlsys.neural-architecture-search", "relation": "determines", "target": "mlsys.efficiency-frontier", "statement": "searches for points on", "conditions": "When the search objective combines quality and a stated cost measure on the target platform." }, { "source": "mlsys.hardware-aware-nas", "relation": "uses", "target": "mlsys.analytical-performance-model", "statement": "uses latency predictors from", "conditions": "When on-device measurement of every candidate is too slow, so a calibrated cost model ranks candidates." }, { "source": "mlsys.model-hardware-co-design", "relation": "enables", "target": "mlsys.nm-sparsity", "statement": "enables", "conditions": "When the sparsity pattern is chosen to match a sparse matrix-engine instruction, as with 2:4 sparsity on sparse tensor cores." }, { "source": "mlsys.weight-only-quantization", "relation": "reduces", "target": "mlsys.tpot", "statement": "reduces", "conditions": "When small-batch decoding is memory-bandwidth-bound and dequantization is fused into the matrix multiply." }, { "source": "mlsys.weight-only-quantization", "relation": "mitigates", "target": "mlsys.activation-outliers", "statement": "sidesteps", "conditions": "Because activations stay in higher precision, outlier channels never enter a low-bit activation range." }, { "source": "mlsys.transformer", "relation": "produces", "target": "mlsys.activation-outliers", "statement": "develops, at scale,", "conditions": "In large transformers, roughly beyond a few billion parameters, where a few hidden dimensions carry systematic large-magnitude activations." }, { "source": "mlsys.kv-cache-quantization", "relation": "reduces", "target": "mlsys.tpot", "statement": "reduces", "conditions": "When long-context decoding time is dominated by reading the KV cache from memory." }, { "source": "mlsys.kv-cache-quantization", "relation": "enables", "target": "mlsys.batching", "statement": "raises the feasible batch size of", "conditions": "When serving is KV-memory-bound, so freed capacity admits more concurrent sequences." }, { "source": "mlsys.training-goodput", "relation": "reduces", "target": "mlsys.time-to-train", "statement": "reduces", "conditions": "For a fixed model, data, and quality target, since lost and redone work lengthens wall-clock time." }, { "source": "mlsys.inference-load-scenario", "relation": "determines", "target": "mlsys.batching", "statement": "determines the admissible", "conditions": "Offline scenarios permit large batches; server and single-stream scenarios limit batching by their latency constraint." }, { "source": "mlsys.inference-load-scenario", "relation": "uses", "target": "mlsys.arrival-rate", "statement": "specifies the query", "conditions": "In server scenarios, where queries arrive by a stated random process at a target rate." }, { "source": "mlsys.activation-recompute", "relation": "reduces", "target": "mlsys.model-flops-utilization", "statement": "lowers measured", "conditions": "Because recomputed FLOPs are excluded from MFU's model-required count, so MFU falls while hardware FLOPs utilization rises." }, { "source": "mlsys.bottleneck-analysis", "relation": "detects", "target": "mlsys.bound-regime", "statement": "identifies the operating", "conditions": "When per-kernel time, FLOPs, and bytes are measured, so each kernel can be placed relative to the ridge point." }, { "source": "mlsys.job-preemption", "relation": "reduces", "target": "mlsys.training-goodput", "statement": "reduces", "conditions": "When preempted work since the last checkpoint is lost and restart costs are paid on resumption." }, { "source": "mlsys.benchmark", "relation": "uses", "target": "mlsys.energy-measurement", "statement": "uses", "conditions": "For energy or power benchmarks, which must declare sensor class, sampling, and system boundary to be comparable." }, { "source": "mlsys.consistent-cut", "relation": "enables", "target": "mlsys.checkpoint-resharding", "statement": "enables", "conditions": "A sharded checkpoint can be remapped to a new layout only when all shards were saved from the same training step." }, { "source": "mlsys.async-checkpointing", "relation": "requires", "target": "mlsys.consistent-cut", "statement": "requires", "conditions": "When every rank snapshots its shard at the same step boundary before background persistence begins." }, { "source": "mlsys.async-checkpointing", "relation": "increases", "target": "mlsys.training-goodput", "statement": "increases", "conditions": "When the blocking snapshot is much cheaper than the durable write, so training stalls less per checkpoint." }, { "source": "mlsys.circuit-breaker", "relation": "enables", "target": "mlsys.graceful-degradation", "statement": "enables", "conditions": "When an open breaker routes requests to a fallback response rather than returning errors." }, { "source": "mlsys.circuit-breaker", "relation": "reduces", "target": "mlsys.arrival-rate", "statement": "cuts the request rate reaching a failing dependency, lowering its", "conditions": "While the breaker is open, which gives the overloaded dependency time to recover." }, { "source": "mlsys.replication-failover", "relation": "requires", "target": "mlsys.fencing", "statement": "requires", "conditions": "When a failed-over primary may still be alive, so stale writers must be fenced to avoid split brain." }, { "source": "mlsys.replication-failover", "relation": "increases", "target": "mlsys.tco", "statement": "increases", "conditions": "Redundant replicas add capital and operating cost in proportion to the replication factor." }, { "source": "mlsys.mtbf", "relation": "limits", "target": "mlsys.training-goodput", "statement": "bounds", "conditions": "For large synchronous jobs, where job MTBF shrinks with component count and each failure costs rework plus restart." }, { "source": "mlsys.distributed-tracing", "relation": "detects", "target": "mlsys.gray-failure", "statement": "detects", "conditions": "When traces record the latency and errors that callers actually experience rather than health-check results." }, { "source": "mlsys.network-topology", "relation": "determines", "target": "mlsys.failure-domain", "statement": "determines", "conditions": "Components behind a shared switch, rack, or power feed fail together, so the topology fixes the failure domains." }, { "source": "mlsys.abft", "relation": "uses", "target": "mlsys.matrix-multiply", "statement": "carries checksums through", "conditions": "For linear-algebra kernels whose input checksums predict output checksums." }, { "source": "mlsys.abft", "relation": "increases", "target": "mlsys.execution-time", "statement": "adds checksum overhead to", "conditions": "The overhead is small relative to large matrix multiplications but nonzero." }, { "source": "mlsys.game-day", "relation": "measures", "target": "mlsys.alerting", "statement": "tests whether alerts fire and reach responders through", "conditions": "When the exercise uses real paging and runbook paths." }, { "source": "mlsys.hardware-fault-injection", "relation": "specializes", "target": "mlsys.failure-injection", "statement": "is a kind of", "conditions": "When the injected faults are bit flips or stuck-at errors in model state rather than service-level faults." }, { "source": "mlsys.rollout", "relation": "reduces", "target": "mlsys.blast-radius", "statement": "reduces", "conditions": "When each stage exposes a small fraction of traffic and gates promotion on health signals." }, { "source": "mlsys.least-privilege", "relation": "reduces", "target": "mlsys.blast-radius", "statement": "reduces", "conditions": "When a compromised or faulty component holds only the permissions its task requires." }, { "source": "mlsys.transfer-learning", "relation": "increases", "target": "mlsys.backdoor", "statement": "increases exposure to", "conditions": "When pretrained weights come from untrusted parties, since backdoors can survive fine-tuning on clean data." }, { "source": "mlsys.guardrails", "relation": "mitigates", "target": "mlsys.jailbreak", "statement": "partially mitigates", "conditions": "Input and output classifiers catch known patterns but can be evaded by novel or obfuscated prompts." }, { "source": "mlsys.rlhf", "relation": "mitigates", "target": "mlsys.jailbreak", "statement": "partially mitigates", "conditions": "Safety training reduces disallowed outputs but fails under competing objectives and mismatched generalization." }, { "source": "mlsys.adversarial-training", "relation": "increases", "target": "mlsys.time-to-train", "statement": "increases", "conditions": "Each training step runs an inner attack, multiplying compute by roughly the number of attack steps." }, { "source": "mlsys.distillation", "relation": "limits", "target": "mlsys.model-watermarking", "statement": "can strip parameter-embedded", "conditions": "For watermarks embedded in weights rather than in behavior on trigger inputs, since a distilled student has new weights." }, { "source": "mlsys.artifact-signing", "relation": "mitigates", "target": "mlsys.supply-chain-compromise", "statement": "mitigates", "conditions": "When consumers verify signatures and provenance against trusted identities before loading artifacts." }, { "source": "mlsys.model-registry", "relation": "uses", "target": "mlsys.artifact-signing", "statement": "uses", "conditions": "When the registry signs artifacts on promotion and verifies them before deployment." }, { "source": "mlsys.isolation", "relation": "mitigates", "target": "mlsys.unsafe-deserialization", "statement": "contains the effects of", "conditions": "When untrusted model files are loaded inside a sandbox with no credentials or network access." }, { "source": "mlsys.generalization", "relation": "reduces", "target": "mlsys.membership-inference", "statement": "lowers the advantage of", "conditions": "Attack advantage grows with the train-test generalization gap, so better-generalizing models leak less membership signal." }, { "source": "mlsys.dp-sgd", "relation": "mitigates", "target": "mlsys.model-inversion", "statement": "mitigates", "conditions": "For reconstruction of individual training records; inference of population-level attributes is not bounded by DP." }, { "source": "mlsys.secure-aggregation", "relation": "mitigates", "target": "mlsys.model-inversion", "statement": "blocks gradient-based", "conditions": "When the server could otherwise reconstruct a client's data from its individual update." }, { "source": "mlsys.data-retention", "relation": "requires", "target": "mlsys.machine-unlearning", "statement": "requires", "conditions": "When deletion obligations extend to models trained on the deleted records." }, { "source": "mlsys.remote-attestation", "relation": "detects", "target": "mlsys.supply-chain-compromise", "statement": "detects", "conditions": "For platform firmware or software whose measured state differs from trusted reference values." }, { "source": "mlsys.multi-tenancy", "relation": "increases", "target": "mlsys.side-channel", "statement": "increases exposure to", "conditions": "When tenants share accelerators, caches, memory, or interconnect whose contention is observable." }, { "source": "mlsys.side-channel", "relation": "causes", "target": "mlsys.privacy-leakage", "statement": "can cause", "conditions": "When observed timing, power, or memory behavior correlates with secret inputs or model parameters." }, { "source": "mlsys.he-inference", "relation": "increases", "target": "mlsys.execution-time", "statement": "multiplies", "conditions": "Homomorphic evaluation costs orders of magnitude more compute than plaintext inference, especially for nonlinear layers." }, { "source": "mlsys.secure-computation-inference", "relation": "increases", "target": "mlsys.execution-time", "statement": "increases", "conditions": "Latency is dominated by communication rounds and garbled-circuit evaluation of nonlinear layers." }, { "source": "mlsys.pue", "relation": "increases", "target": "mlsys.operational-carbon", "statement": "multiplies IT energy into", "conditions": "Facility overhead for cooling and power conversion scales IT energy by PUE before carbon intensity applies." }, { "source": "mlsys.emission-scopes", "relation": "determines", "target": "mlsys.operational-carbon", "statement": "classifies purchased-electricity emissions (Scope 2) in", "conditions": "For an operator buying grid electricity under the GHG Protocol." }, { "source": "mlsys.lifecycle-assessment", "relation": "measures", "target": "mlsys.water-footprint", "statement": "measures", "conditions": "When the assessment's inventory includes on-site cooling and off-site generation water." }, { "source": "mlsys.lifecycle-assessment", "relation": "measures", "target": "mlsys.e-waste", "statement": "accounts end-of-life impacts of", "conditions": "When the system boundary includes disposal and recycling stages." }, { "source": "mlsys.spot-capacity", "relation": "reduces", "target": "mlsys.tco", "statement": "reduces", "conditions": "When workloads tolerate preemption through checkpointing or elasticity so the discount is not lost to rework." }, { "source": "mlsys.model-routing", "relation": "reduces", "target": "mlsys.cost-per-token", "statement": "lowers the average", "conditions": "When a router sends requests a cheaper model can handle to that model while meeting the quality target." }, { "source": "mlsys.multi-tenancy", "relation": "requires", "target": "mlsys.cost-attribution", "statement": "requires", "conditions": "When shared infrastructure must be charged back to the teams or workloads that consume it." }, { "source": "mlsys.cost-attribution", "relation": "uses", "target": "mlsys.idle-dynamic-power", "statement": "allocates shared idle cost using", "conditions": "When provisioned but idle capacity must be charged to owners by an explicit rule." }, { "source": "mlsys.idle-dynamic-power", "relation": "determines", "target": "mlsys.energy-efficiency", "statement": "determines", "conditions": "At low load, idle power dominates, so poorly energy-proportional hardware loses efficiency." }, { "source": "mlsys.stpa", "relation": "uses", "target": "mlsys.feedback-control", "statement": "models the system as", "conditions": "STPA represents the system as controllers, control actions, and feedback paths." }, { "source": "mlsys.stpa", "relation": "detects", "target": "mlsys.functional-insufficiency", "statement": "detects", "conditions": "When hazards arise from unsafe control actions or flawed process models without any component fault." }, { "source": "mlsys.fmea", "relation": "uses", "target": "mlsys.failure-model", "statement": "enumerates component failure modes from", "conditions": "FMEA is bottom-up over declared component failure modes." }, { "source": "mlsys.functional-safety", "relation": "requires", "target": "mlsys.hazard-analysis", "statement": "requires", "conditions": "ISO 26262 requires hazard analysis and risk assessment before safety goals are set." }, { "source": "mlsys.functional-safety", "relation": "uses", "target": "mlsys.safety-integrity-level", "statement": "uses", "conditions": "Safety goals are assigned an integrity level, such as an ASIL, that sets development rigor." }, { "source": "mlsys.functional-safety", "relation": "uses", "target": "mlsys.fmea", "statement": "uses", "conditions": "When component failure modes must be traced to effects on safety goals." }, { "source": "mlsys.safety-filter", "relation": "uses", "target": "mlsys.model-predictive-control", "statement": "uses", "conditions": "For predictive safety filters that solve a constrained finite-horizon problem to find the nearest safe action." }, { "source": "mlsys.control-barrier-function", "relation": "requires", "target": "mlsys.dynamics", "statement": "requires a model of", "conditions": "The barrier condition holds only under the modeled dynamics and bounded disturbance." }, { "source": "mlsys.simplex-architecture", "relation": "requires", "target": "mlsys.mixed-criticality", "statement": "requires", "conditions": "The verified safety controller and decision module must keep their timing even when the complex controller overruns." }, { "source": "mlsys.minimal-risk-condition", "relation": "specializes", "target": "mlsys.graceful-degradation", "statement": "is a kind of", "conditions": "For physical systems whose degraded mode is a predefined reduced-capability safe state." }, { "source": "mlsys.minimal-risk-condition", "relation": "alternative-to", "target": "mlsys.authority-handover", "statement": "is an alternative to", "conditions": "When the system can no longer operate safely, it can either hand control to a human or reach a minimal-risk state itself." }, { "source": "mlsys.operating-domain", "relation": "determines", "target": "mlsys.scenario-based-testing", "statement": "scopes the scenario catalog of", "conditions": "Scenarios and parameter ranges are drawn from the declared operational design domain." }, { "source": "mlsys.safety-case", "relation": "uses", "target": "mlsys.scenario-based-testing", "statement": "draws coverage evidence from", "conditions": "When scenario coverage is traced to hazards and operating-domain conditions." }, { "source": "mlsys.falsification", "relation": "enables", "target": "mlsys.rare-event-validation", "statement": "enables", "conditions": "Failure regions found by falsification can seed importance sampling for failure-probability estimates." }, { "source": "mlsys.falsification", "relation": "alternative-to", "target": "mlsys.formal-verification", "statement": "is an alternative to", "conditions": "For systems too complex for formal proof, where searching for counterexamples replaces proving their absence." }, { "source": "mlsys.neural-network-verification", "relation": "alternative-to", "target": "mlsys.robustness-evaluation", "statement": "is a certified alternative to empirical", "conditions": "For properties over input regions small enough for the verifier to scale." }, { "source": "mlsys.statistical-uncertainty", "relation": "limits", "target": "mlsys.rare-event-validation", "statement": "bounds failure rates with", "conditions": "When the number of observed failures is small, so confidence bounds dominate the estimate." }, { "source": "mlsys.xil-testing", "relation": "measures", "target": "mlsys.feedback-delay", "statement": "measures", "conditions": "When the real compute and hardware run in the loop, exposing timing that pure simulation omits." }, { "source": "mlsys.xil-testing", "relation": "specializes", "target": "mlsys.integration-testing", "statement": "is a kind of", "conditions": "Each rung tests the assembled software or hardware against a simulated or recorded environment." }, { "source": "mlsys.goal-structuring-notation", "relation": "enables", "target": "mlsys.algorithmic-audit", "statement": "enables", "conditions": "When auditors review the assurance argument, not only its evidence artifacts." }, { "source": "mlsys.safety-performance-indicator", "relation": "detects", "target": "mlsys.functional-insufficiency", "statement": "detects", "conditions": "When indicators count field events, such as near misses or perception misses, inside the operating domain." }, { "source": "mlsys.compensation", "relation": "alternative-to", "target": "mlsys.rollback", "statement": "is an alternative to", "conditions": "Rollback restores software or configuration state; compensation remedies effects when such a reversal is unavailable." }, { "source": "mlsys.runtime-assurance", "relation": "uses", "target": "mlsys.stopping-envelope", "statement": "checks the current state against", "conditions": "When the fallback is a stop and the monitor runs fast enough to trigger it before the state leaves the envelope." }, { "source": "mlsys.hardware-fault-injection", "relation": "measures", "target": "mlsys.abft", "statement": "measures the detection coverage of", "conditions": "When injected faults hit the operations the checksums protect, at realistic rates and locations." }, { "source": "mlsys.individual-fairness", "relation": "specializes", "target": "mlsys.fairness", "statement": "is a kind of", "conditions": "When the fairness criterion is stated over pairs of similar individuals rather than groups." }, { "source": "mlsys.individual-fairness", "relation": "limits", "target": "mlsys.decision-policy", "statement": "constrains", "conditions": "When a task-specific similarity metric is declared and accepted by stakeholders." }, { "source": "mlsys.fairness-incompatibility", "relation": "limits", "target": "mlsys.calibration", "statement": "limits per-group", "conditions": "When base rates differ and error-rate parity is also required." }, { "source": "mlsys.fairness-incompatibility", "relation": "limits", "target": "mlsys.fairness", "statement": "limits which criteria can jointly hold in", "conditions": "When base rates differ across groups and the predictor is imperfect." }, { "source": "mlsys.bias-mitigation", "relation": "implements", "target": "mlsys.fairness", "statement": "implements", "conditions": "When the intervention targets a declared criterion and is re-evaluated on held-out data." }, { "source": "mlsys.bias-mitigation", "relation": "uses", "target": "mlsys.data-selection", "statement": "uses reweighting and resampling from", "conditions": "For pre-processing methods that change the training distribution across groups." }, { "source": "mlsys.feature-attribution", "relation": "detects", "target": "mlsys.leakage", "statement": "detects", "conditions": "When a feature unavailable at prediction time dominates attributions." }, { "source": "mlsys.counterfactual-explanation", "relation": "alternative-to", "target": "mlsys.feature-attribution", "statement": "is an alternative to", "conditions": "Both explain one decision post hoc; counterfactuals give actionable changes, attributions give per-feature contributions." }, { "source": "mlsys.interpretable-model", "relation": "enables", "target": "mlsys.contestability", "statement": "enables", "conditions": "When affected people or reviewers can read the decision logic that produced their outcome." }, { "source": "mlsys.interpretable-model", "relation": "enables", "target": "mlsys.algorithmic-audit", "statement": "enables", "conditions": "When auditors can inspect the model directly instead of relying on post-hoc approximations." }, { "source": "mlsys.model-registry", "relation": "uses", "target": "mlsys.model-card", "statement": "stores and versions", "conditions": "When documentation is required for promotion alongside the model artifact." }, { "source": "mlsys.algorithmic-audit", "relation": "uses", "target": "mlsys.datasheet", "statement": "draws evidence from", "conditions": "Datasheets document data provenance and composition but do not replace access to the data." }, { "source": "mlsys.automation-bias", "relation": "limits", "target": "mlsys.approval-gate", "statement": "can reduce to rubber stamps", "conditions": "When reviewers see many correct proposals, weak error cues, and time pressure." }, { "source": "mlsys.automation-bias", "relation": "limits", "target": "mlsys.authority-handover", "statement": "can degrade", "conditions": "When operators lose vigilance and manual skill during long automated stretches." }, { "source": "mlsys.ai-risk-management", "relation": "uses", "target": "mlsys.red-teaming", "statement": "uses", "conditions": "When red-team findings feed the measure and manage functions of the risk process." }, { "source": "mlsys.risk-tiered-regulation", "relation": "requires", "target": "mlsys.impact-assessment", "statement": "requires", "conditions": "For deployers of high-risk systems in categories where a fundamental-rights impact assessment is required." }, { "source": "mlsys.impact-assessment", "relation": "determines", "target": "mlsys.fairness", "statement": "identifies the affected groups and criteria for", "conditions": "When the assessment precedes deployment and is revisited after material changes." }, { "source": "mlsys.planning", "relation": "uses", "target": "mlsys.inference-time-search", "statement": "uses", "conditions": "When the agent expands and scores alternative partial plans rather than committing to one chain." }, { "source": "mlsys.agent-evaluation", "relation": "measures", "target": "mlsys.planning", "statement": "measures plan quality through", "conditions": "When intermediate steps, not only final outcomes, are scored across the trajectory." }, { "source": "mlsys.agent-budget", "relation": "limits", "target": "mlsys.planning", "statement": "bounds the search depth of", "conditions": "When turn, token, or time limits cap how many alternatives a plan can explore." }, { "source": "mlsys.tool-contract", "relation": "determines", "target": "mlsys.idempotency", "statement": "declares whether retries are safe through", "conditions": "When the contract states an operation's idempotency key or its at-most-once semantics." }, { "source": "mlsys.robot-perception", "relation": "requires", "target": "mlsys.sensing", "statement": "requires calibrated", "conditions": "Detections and depth are only as accurate as the intrinsic, extrinsic, and timing calibration of the sensors." }, { "source": "mlsys.sensor-fusion", "relation": "increases", "target": "mlsys.state-observability", "statement": "increases", "conditions": "When an added modality measures a state the others cannot distinguish, such as scale for monocular vision." }, { "source": "mlsys.state-observability", "relation": "limits", "target": "mlsys.slam", "statement": "bounds drift correction in", "conditions": "Absolute pose and heading are unobservable from relative measurements alone, so drift persists without loop closure or external references." }, { "source": "mlsys.contact-compliance", "relation": "limits", "target": "mlsys.sim-to-real", "statement": "can widen the gap in", "conditions": "Contact forces depend on stiffness, friction, and geometry that simulators approximate poorly." }, { "source": "mlsys.model-context-protocol", "relation": "increases", "target": "mlsys.prompt-injection", "statement": "widens the surface for", "conditions": "When tool descriptions and results from third-party servers enter the model's context as if trusted." }, { "source": "mlsys.model-context-protocol", "relation": "uses", "target": "mlsys.authorization", "statement": "uses", "conditions": "When servers require scoped, delegated credentials before exposing tools or resources." }, { "source": "mlsys.agent-evaluation", "relation": "measures", "target": "mlsys.computer-use", "statement": "measures", "conditions": "When tasks run in real applications and success is checked from the resulting system state." }, { "source": "mlsys.agent-harness", "relation": "uses", "target": "mlsys.function-calling", "statement": "dispatches tool actions through", "conditions": "When the model emits structured calls that the harness validates and executes." }, { "source": "mlsys.agent-harness", "relation": "uses", "target": "mlsys.agent-budget", "statement": "enforces stopping rules from", "conditions": "The harness, not the model, applies turn, token, time, and spend limits." }, { "source": "mlsys.agent-budget", "relation": "limits", "target": "mlsys.trajectory", "statement": "bounds the length of", "conditions": "When limits on turns, tokens, or wall-clock time stop the run before completion." }, { "source": "mlsys.durable-execution", "relation": "specializes", "target": "mlsys.workflow-orchestration", "statement": "is a kind of", "conditions": "When each step's intent and result are logged so the workflow resumes after a crash without redoing completed steps." }, { "source": "mlsys.deterministic-replay", "relation": "requires", "target": "mlsys.llm-call-tracing", "statement": "requires recorded model outputs and tool results from", "conditions": "Because re-sampling the model or re-calling tools would not reproduce the original trajectory." }, { "source": "mlsys.deterministic-replay", "relation": "enables", "target": "mlsys.failure-attribution", "statement": "enables", "conditions": "When a failing trajectory can be re-run step by step with its original inputs." }, { "source": "mlsys.suspend-resume", "relation": "uses", "target": "mlsys.durable-execution", "statement": "persists waiting state through", "conditions": "When a trajectory must survive process restarts while waiting on a human or external event." }, { "source": "mlsys.shared-agent-state", "relation": "part-of", "target": "mlsys.multi-agent-orchestration", "statement": "is part of", "conditions": "When several agents read and write task state under one orchestration topology." }, { "source": "mlsys.shared-agent-state", "relation": "causes", "target": "mlsys.coordination-failure", "statement": "can cause", "conditions": "When concurrent writes or stale reads go uncontrolled, so agents act on conflicting views of the task." }, { "source": "mlsys.llm-call-tracing", "relation": "detects", "target": "mlsys.coordination-failure", "statement": "detects", "conditions": "When handoffs, delegated instructions, and per-agent outputs are recorded as linked spans." }, { "source": "mlsys.correlated-agent-errors", "relation": "limits", "target": "mlsys.self-consistency", "statement": "limits the benefit of", "conditions": "When the sampled answers or voting agents share a base model and prompt, so their errors are not independent." }, { "source": "mlsys.correlated-agent-errors", "relation": "limits", "target": "mlsys.llm-judge", "statement": "weakens the independence of", "conditions": "When the judge shares a base model with the system it grades." }, { "source": "mlsys.robot-perception", "relation": "causes", "target": "mlsys.functional-insufficiency", "statement": "can cause", "conditions": "When perception limits, such as missed detections in rare conditions, cause hazards without any fault." }, { "source": "mlsys.observation-age", "relation": "requires", "target": "mlsys.time-synchronization", "statement": "makes measurable", "conditions": "Observation age can be computed only when capture and use timestamps share a synchronized clock." }, { "source": "mlsys.recursive-filtering", "relation": "uses", "target": "mlsys.dynamics", "statement": "predicts state with a model of", "conditions": "The prediction step propagates the estimate through modeled dynamics between measurements." }, { "source": "mlsys.sensor-fusion", "relation": "uses", "target": "mlsys.recursive-filtering", "statement": "uses", "conditions": "When fused measurements arrive asynchronously and are combined through prediction and correction steps." }, { "source": "mlsys.world-model", "relation": "enables", "target": "mlsys.reinforcement-learning", "statement": "enables sample-efficient", "conditions": "When policies are trained on imagined rollouts from the learned model rather than only on environment interaction." }, { "source": "mlsys.motion-planning", "relation": "requires", "target": "mlsys.state-estimation", "statement": "requires", "conditions": "Plans are valid only relative to the estimated pose and map they start from." }, { "source": "mlsys.motion-planning", "relation": "uses", "target": "mlsys.slam", "statement": "plans over maps from", "conditions": "When the environment is not known in advance and the map is built online." }, { "source": "mlsys.mixed-criticality", "relation": "uses", "target": "mlsys.real-time-scheduling", "statement": "uses", "conditions": "Schedulability is analyzed with criticality-dependent WCET estimates for each task." }, { "source": "mlsys.real-time-scheduling", "relation": "limits", "target": "mlsys.utilization", "statement": "bounds", "conditions": "Rate-monotonic scheduling guarantees deadlines only when processor utilization stays below the Liu-Layland bound." }, { "source": "mlsys.vla-model", "relation": "uses", "target": "mlsys.transfer-learning", "statement": "transfers web-scale vision-language knowledge through", "conditions": "When the policy is fine-tuned from a pretrained vision-language backbone." }, { "source": "mlsys.vla-model", "relation": "uses", "target": "mlsys.teleoperation-data", "statement": "is fine-tuned on", "conditions": "When robot action labels come from human-driven demonstrations." }, { "source": "mlsys.synthetic-data", "relation": "alternative-to", "target": "mlsys.teleoperation-data", "statement": "is an alternative to", "conditions": "Simulated demonstrations are cheaper to scale but carry a sim-to-real gap that teleoperated demonstrations avoid." }, { "source": "mlsys.action-chunking", "relation": "increases", "target": "mlsys.observation-age", "statement": "increases, for actions late in a chunk,", "conditions": "When chunks execute open loop, so later actions rest on older observations." }, { "source": "mlsys.imitation-learning", "relation": "uses", "target": "mlsys.action-chunking", "statement": "reduces compounding error with", "conditions": "When the policy predicts short action sequences rather than single steps." }, { "source": "mlsys.domain-randomization", "relation": "specializes", "target": "mlsys.data-augmentation", "statement": "is a kind of", "conditions": "Randomized simulator parameters act as label-preserving transformations of training scenes." }, { "source": "mlsys.domain-randomization", "relation": "mitigates", "target": "mlsys.distribution-shift", "statement": "mitigates", "conditions": "When the randomized ranges cover the real-world parameters without making the task unlearnable." }, { "source": "mlsys.safety-filter", "relation": "limits", "target": "mlsys.vla-model", "statement": "constrains the commanded actions of", "conditions": "When the filter runs on every action at control rate with authority over the learned policy." }, { "source": "mlsys.world-model", "relation": "uses", "target": "mlsys.self-supervised-learning", "statement": "is trained to predict future observations by", "conditions": "When the model learns from action-conditioned sequences without task labels." }, { "source": "mlsys.time-synchronization", "relation": "part-of", "target": "mlsys.sensing", "statement": "is part of", "conditions": "Sensing establishes how observations relate to time as well as to physical quantities and coordinates." }, { "source": "mlsys.diffusion-model", "relation": "specializes", "target": "mlsys.neural-network", "statement": "is a kind of", "conditions": "Diffusion models are neural networks trained to reverse a gradual noising process over many denoising steps." }, { "source": "mlsys.compute-in-memory", "relation": "reduces", "target": "mlsys.data-movement-energy", "statement": "lowers by computing where weights are stored", "conditions": "Weight-stationary matrix-vector workloads whose weights fit in the memory arrays." }, { "source": "mlsys.compute-in-memory", "relation": "limits", "target": "mlsys.precision", "statement": "caps the effective bit width of", "conditions": "Analog implementations, where device noise, drift, and converter resolution bound precision." }, { "source": "mlsys.emerging-nvm", "relation": "enables", "target": "mlsys.compute-in-memory", "statement": "provides programmable analog cells for", "conditions": "Device conductance can be programmed to represent weight values." }, { "source": "mlsys.emerging-nvm", "relation": "alternative-to", "target": "mlsys.on-chip-sram", "statement": "competes for on-chip weight storage with", "conditions": "Density and standby power outweigh write endurance and write latency." }, { "source": "mlsys.embedded-dsl", "relation": "produces", "target": "mlsys.computation-graph", "statement": "builds", "conditions": "Staged or traced execution rather than immediate evaluation." }, { "source": "mlsys.embedded-dsl", "relation": "enables", "target": "mlsys.compilation", "statement": "exposes whole programs to", "conditions": "The DSL restricts host-language features that cannot be staged." }, { "source": "mlsys.kernel-dsl", "relation": "specializes", "target": "mlsys.embedded-dsl", "statement": "is a kernel-level case of", "conditions": "Kernel DSLs embedded in a host language" }, { "source": "mlsys.differentiable-programming", "relation": "requires", "target": "mlsys.autodiff", "statement": "relies on", "conditions": "Derivatives must propagate through control flow and data structures." }, { "source": "mlsys.function-transformations", "relation": "implements", "target": "mlsys.differentiable-programming", "statement": "realizes through a composable grad transformation", "conditions": "Programs written as pure array functions." }, { "source": "mlsys.persistent-kernel", "relation": "reduces", "target": "mlsys.dispatch", "statement": "removes per-operator launch overhead from", "conditions": "Small-batch, latency-bound workloads made of many short kernels." }, { "source": "mlsys.persistent-kernel", "relation": "alternative-to", "target": "mlsys.graph-replay", "statement": "is an alternative way to remove launch overhead to", "conditions": "Persistent kernels also overlap work across operator boundaries inside one launch." }, { "source": "mlsys.persistent-kernel", "relation": "specializes", "target": "mlsys.fusion", "statement": "is an extreme form of", "conditions": "Fusion extended across operator and layer boundaries into a single launch." }, { "source": "mlsys.graph-superoptimization", "relation": "part-of", "target": "mlsys.compilation", "statement": "is an optimization stage within", "conditions": "Rewrites are verified equivalent before code generation." }, { "source": "mlsys.graph-superoptimization", "relation": "uses", "target": "mlsys.intermediate-representation", "statement": "searches rewrites expressed in", "conditions": "Graph- or kernel-level IR with defined operator semantics." }, { "source": "mlsys.graph-superoptimization", "relation": "enables", "target": "mlsys.fusion", "statement": "discovers new opportunities for", "conditions": "Search spans kernel and thread-block levels as well as the graph level." }, { "source": "mlsys.llm-training-framework", "relation": "implements", "target": "mlsys.hybrid-parallel", "statement": "packages", "conditions": "Parallelism degrees are set by configuration and mapped to the device topology." }, { "source": "mlsys.llm-training-framework", "relation": "uses", "target": "mlsys.state-sharding", "statement": "commonly applies", "conditions": "Optimizer and parameter state exceed per-device memory." }, { "source": "mlsys.llm-training-framework", "relation": "uses", "target": "mlsys.activation-recompute", "statement": "commonly applies", "conditions": "Activation memory limits sequence length or micro-batch size." }, { "source": "mlsys.cloud-service-model", "relation": "determines", "target": "mlsys.tco", "statement": "shifts the cost structure of", "conditions": "Comparing rented infrastructure" }, { "source": "mlsys.cloud-service-model", "relation": "uses", "target": "mlsys.multi-tenancy", "statement": "relies on resource pooling through", "conditions": "Providers serve many customers from shared physical resources." }, { "source": "mlsys.lakehouse", "relation": "uses", "target": "mlsys.object-storage", "statement": "stores table files in", "conditions": "Lakehouse designs over open file formats." }, { "source": "mlsys.lakehouse", "relation": "enables", "target": "mlsys.data-versioning", "statement": "provides snapshot time travel for", "conditions": "Table formats that retain prior snapshots." }, { "source": "mlsys.lakehouse", "relation": "uses", "target": "mlsys.dataset-storage-layout", "statement": "stores data in columnar layouts such as", "conditions": "Open columnar formats such as Parquet." }, { "source": "mlsys.training-power-swings", "relation": "limits", "target": "mlsys.power-oversubscription", "statement": "removes headroom assumed by", "conditions": "Swings are coincident across accelerators, defeating non-coincident-peak assumptions." }, { "source": "mlsys.facility-energy-storage", "relation": "mitigates", "target": "mlsys.training-power-swings", "statement": "buffers", "conditions": "Storage sized and controlled to respond at the swing frequency." }, { "source": "mlsys.power-capping", "relation": "mitigates", "target": "mlsys.training-power-swings", "statement": "smooths", "conditions": "Device power caps and floors applied to bound ramp rates." }, { "source": "mlsys.facility-energy-storage", "relation": "increases", 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"Rebalanced training shifts predicted probabilities away from true base rates unless corrected." }, { "source": "mlsys.feature-hashing", "relation": "specializes", "target": "mlsys.features", "statement": "is a technique within", "conditions": "Categorical inputs with large or open vocabularies." }, { "source": "mlsys.feature-hashing", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "bounds", "conditions": "Weight or embedding tables indexed by hash bucket." }, { "source": "mlsys.feature-cross", "relation": "specializes", "target": "mlsys.features", "statement": "is a technique within", "conditions": "Interactions among categorical inputs." }, { "source": "mlsys.feature-cross", "relation": "uses", "target": "mlsys.feature-hashing", "statement": "is often bucketed by", "conditions": "Crosses of high-cardinality features whose joint vocabulary would explode." }, { "source": "mlsys.linear-model", "relation": "uses", "target": "mlsys.feature-cross", "statement": 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"mlsys.model-selection", "relation": "uses", "target": "mlsys.cross-validation", "statement": "estimates candidate performance with", "conditions": "Data is too small for a dedicated validation split." }, { "source": "mlsys.model-selection", "relation": "causes", "target": "mlsys.selection-bias", "statement": "can inflate estimates through", "conditions": "Selection and final assessment reuse the same validation data." }, { "source": "mlsys.model-selection", "relation": "uses", "target": "mlsys.baseline", "statement": "compares candidates against", "conditions": "The baseline is evaluated under the same protocol." }, { "source": "mlsys.hyperparameter-search", "relation": "part-of", "target": "mlsys.model-selection", "statement": "is a stage within", "conditions": "Hyperparameters are chosen on validation data." }, { "source": "mlsys.pretraining", "relation": "uses", "target": "mlsys.self-supervised-learning", "statement": "optimizes an objective from", "conditions": "Unlabeled corpora 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"target": "mlsys.least-privilege", "statement": "applies to network access", "conditions": "Only destinations the task needs are allowed." }, { "source": "mlsys.egress-control", "relation": "mitigates", "target": "mlsys.privacy-leakage", "statement": "blocks exfiltration channels for", "conditions": "Leakage would otherwise travel over outbound connections." }, { "source": "mlsys.ephemeral-sandbox", "relation": "uses", "target": "mlsys.egress-control", "statement": "restricts outbound network access with", "conditions": "The sandbox has network access." }, { "source": "mlsys.information-flow-control", "relation": "mitigates", "target": "mlsys.prompt-injection", "statement": "prevents untrusted data from steering actions in", "conditions": "Data from tools and documents carries low-integrity labels." }, { "source": "mlsys.information-flow-control", "relation": "mitigates", "target": "mlsys.privacy-leakage", "statement": "blocks labeled secrets from", "conditions": "Confidential data is 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"mlsys.credit-assignment", "statement": "provides step-level signals for", "conditions": "Intermediate steps can be judged." }, { "source": "mlsys.actuator-limits", "relation": "determines", "target": "mlsys.trajectory-feasibility", "statement": "sets which motions satisfy", "conditions": "Planned motions demand force or speed near the actuator's capability." }, { "source": "mlsys.model-predictive-control", "relation": "uses", "target": "mlsys.actuator-limits", "statement": "encodes as input constraints", "conditions": "Limits are known and modeled." }, { "source": "mlsys.watchdog-timer", "relation": "part-of", "target": "mlsys.runtime-assurance", "statement": "is a liveness mechanism within", "conditions": "The monitored component can hang or stall." }, { "source": "mlsys.watchdog-timer", "relation": "uses", "target": "mlsys.minimal-risk-condition", "statement": "triggers on expiry", "conditions": "A safe fallback state is defined." }, { "source": "mlsys.lease", "relation": "alternative-to", "target": "mlsys.watchdog-timer", "statement": "expires authority by time like", "conditions": "Authority must lapse when renewal stops." }, { "source": "mlsys.closed-loop-evaluation", "relation": "detects", "target": "mlsys.compounding-error", "statement": "reveals", "conditions": "Errors alter later observations, unlike open-loop replay." }, { "source": "mlsys.xil-testing", "relation": "implements", "target": "mlsys.closed-loop-evaluation", "statement": "provides simulated loops for", "conditions": "The environment model reacts to the system's actions." }, { "source": "mlsys.event-data-recorder", "relation": "enables", "target": "mlsys.failure-attribution", "statement": "supplies reconstruction evidence for", "conditions": "Records cover the interval before and after the event." }, { "source": "mlsys.safety-performance-indicator", "relation": "uses", "target": "mlsys.event-data-recorder", "statement": "is computed from events logged by", "conditions": "Field events 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"conditions": "Cancelled work already produced side effects." }, { "source": "mlsys.agent-loop-detection", "relation": "part-of", "target": "mlsys.agent-harness", "statement": "is a progress monitor within", "conditions": "The harness can inspect the trajectory between steps." }, { "source": "mlsys.agent-loop-detection", "relation": "detects", "target": "mlsys.coordination-failure", "statement": "flags looping instances of", "conditions": "Agents repeat steps or hand work back and forth." }, { "source": "mlsys.agent-loop-detection", "relation": "alternative-to", "target": "mlsys.agent-budget", "statement": "stops stuck runs earlier than", "conditions": "Progress can be judged before a budget is exhausted." }, { "source": "mlsys.task-and-motion-planning", "relation": "uses", "target": "mlsys.motion-planning", "statement": "checks each symbolic step with", "conditions": "Actions have geometric preconditions." }, { "source": "mlsys.task-and-motion-planning", "relation": "requires", 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proposed." }, { "source": "mlsys.pruning", "relation": "uses", "target": "mlsys.sparse-tensor-kernels", "statement": "turns unstructured zeros into speedup for", "conditions": "Sparsity is high enough to overcome irregular access." }, { "source": "mlsys.sparse-tensor-kernels", "relation": "uses", "target": "mlsys.tensor-layout", "statement": "stores nonzeros in compressed formats described by", "conditions": "Nonzeros are kept in formats such as CSR or coordinate lists." }, { "source": "mlsys.local-sgd", "relation": "specializes", "target": "mlsys.data-parallel", "statement": "is a periodically synchronized form of", "conditions": "Workers hold full model replicas." }, { "source": "mlsys.local-sgd", "relation": "alternative-to", "target": "mlsys.gradient-compression", "statement": "reduces communication by frequency rather than size as in", "conditions": "Bandwidth between workers is scarce." }, { "source": "mlsys.federated-averaging", "relation": "specializes", "target": "mlsys.local-sgd", "statement": "is a federated instance of", "conditions": "Clients run several local epochs before averaging." }, { "source": "mlsys.expert-placement", "relation": "part-of", "target": "mlsys.expert-parallel", "statement": "is a placement decision within", "conditions": "Experts are distributed across devices." }, { "source": "mlsys.expert-placement", "relation": "mitigates", "target": "mlsys.stragglers", "statement": "relieves overloaded devices that become", "conditions": "Routing concentrates tokens on a few experts." }, { "source": "mlsys.expert-placement", "relation": "alternative-to", "target": "mlsys.load-balancing-loss", "statement": "balances load in the system rather than the model as in", "conditions": "Routing remains skewed despite training-time balancing." }, { "source": "mlsys.memory-efficient-optimizer", "relation": "specializes", "target": "mlsys.optimizer", "statement": "is a reduced-state form of", "conditions": "Full adaptive state exceeds memory." }, { "source": "mlsys.memory-efficient-optimizer", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "shrinks the optimizer-state share of", "conditions": "Optimizer state dominates training memory." }, { "source": "mlsys.memory-efficient-optimizer", "relation": "alternative-to", "target": "mlsys.state-sharding", "statement": "shrinks state rather than distributing it as in", "conditions": "Few devices are available to shard across." }, { "source": "mlsys.profiling", "relation": "produces", "target": "mlsys.execution-trace", "statement": "records", "conditions": "Operator dependencies are captured along with timings." }, { "source": "mlsys.performance-simulation", "relation": "uses", "target": "mlsys.execution-trace", "statement": "replays workloads from", "conditions": "Traces are in a standardized format." }, { "source": "mlsys.offline-rl", "relation": "specializes", "target": "mlsys.reinforcement-learning", "statement": "is a fixed-dataset form of", 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"places tasks with", "conditions": "Machines have multidimensional resource capacity." }, { "source": "mlsys.cluster-manager", "relation": "uses", "target": "mlsys.resource-quota", "statement": "enforces", "conditions": "Multiple teams share the cluster." }, { "source": "mlsys.cluster-manager", "relation": "uses", "target": "mlsys.job-preemption", "statement": "honors priorities through", "conditions": "Higher-priority work arrives while capacity is full." }, { "source": "mlsys.energy-efficiency", "relation": "causes", "target": "mlsys.rebound-effect", "statement": "can trigger", "conditions": "Demand for the service is price-elastic." }, { "source": "mlsys.rebound-effect", "relation": "increases", "target": "mlsys.energy", "statement": "raises total", "conditions": "Demand growth outpaces per-unit savings." }, { "source": "mlsys.data-validation", "relation": "mitigates", "target": "mlsys.data-cascade", "statement": "catches early defects that start", "conditions": "Checks run at ingestion and between stages." }, { "source": "mlsys.data-dependency", "relation": "causes", "target": "mlsys.data-cascade", "statement": "can propagate", "conditions": "Downstream consumers depend on undeclared upstream signals." }, { "source": "mlsys.data-valuation", "relation": "enables", "target": "mlsys.data-selection", "statement": "ranks candidates for", "conditions": "Value estimates are cheaper than retraining on each subset." }, { "source": "mlsys.data-valuation", "relation": "measures", "target": "mlsys.data-quality", "statement": "estimates the contribution side of", "conditions": "A utility metric and baseline model are fixed." }, { "source": "mlsys.diffusion-policy", "relation": "uses", "target": "mlsys.diffusion-model", "statement": "generates actions with", "conditions": "Action distributions are multimodal." }, { "source": "mlsys.diffusion-policy", "relation": "uses", "target": "mlsys.action-chunking", "statement": "predicts action sequences as in", "conditions": "Actions are generated over a short horizon." }, { "source": "mlsys.sensor-spoofing", "relation": "specializes", "target": "mlsys.evasion", "statement": "is a physical-channel form of", "conditions": "The attacker controls signals reaching the sensor." }, { "source": "mlsys.sensor-fusion", "relation": "mitigates", "target": "mlsys.sensor-spoofing", "statement": "cross-checks modalities against", "conditions": "The attacker cannot spoof all modalities consistently." }, { "source": "mlsys.paired-comparison", "relation": "reduces", "target": "mlsys.statistical-uncertainty", "statement": "cancels shared item variance from", "conditions": "Both systems are scored on the same items." }, { "source": "mlsys.paired-comparison", "relation": "uses", "target": "mlsys.baseline", "statement": "tests candidates against", "conditions": "A noninferiority margin is declared in advance." }, { "source": "mlsys.release-gate", "relation": "uses", "target": "mlsys.paired-comparison", "statement": "decides 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"mlsys.rule-based-graph-rewriting", "relation": "alternative-to", "target": "mlsys.graph-superoptimization", "statement": "is the hand-written counterpart to", "conditions": "Both seek semantics-preserving graph transformations; one uses fixed rules, the other searched rewrites." }, { "source": "mlsys.rule-based-graph-rewriting", "relation": "enables", "target": "mlsys.fusion", "statement": "exposes fusible patterns for", "conditions": "Canonicalization places compatible operators adjacent in the graph." }, { "source": "mlsys.sparse-ragged-tensors", "relation": "specializes", "target": "mlsys.tensor-layout", "statement": "is a compressed form of", "conditions": "Storage records only nonzero entries or actual sequence extents." }, { "source": "mlsys.sparse-ragged-tensors", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "shrinks", "conditions": "Density is low or lengths vary widely, so padding would dominate storage." }, { "source": "mlsys.sparse-tensor-kernels", "relation": "uses", "target": "mlsys.sparse-ragged-tensors", "statement": "operates on operands stored in", "conditions": "Kernels read compressed index structures rather than dense arrays." }, { "source": "mlsys.execution-providers", "relation": "uses", "target": "mlsys.model-exchange-format", "statement": "partitions graphs expressed in", "conditions": "The runtime loads a framework-neutral operator graph." }, { "source": "mlsys.on-device-runtime", "relation": "uses", "target": "mlsys.execution-providers", "statement": "delegates subgraphs through", "conditions": "The target device exposes an NPU, GPU, or DSP backend with partial operator coverage." }, { "source": "mlsys.execution-providers", "relation": "increases", "target": "mlsys.dispatch", "statement": "adds host orchestration through", "conditions": "Operator fallback splits the graph and forces cross-backend copies." }, { "source": "mlsys.large-language-model", "relation": "uses", "target": "mlsys.transformer", "statement": "is usually built on", "conditions": "Most current language models use decoder-only Transformer stacks." }, { "source": "mlsys.pretraining", "relation": "produces", "target": "mlsys.large-language-model", "statement": "yields the base weights of", "conditions": "Training uses broad corpora and a self-supervised language-modeling objective." }, { "source": "mlsys.large-language-model", "relation": "specializes", "target": "mlsys.neural-network", "statement": "is a large-scale instance of", "conditions": "The model is a parameterized composition of differentiable operations." }, { "source": "mlsys.inference-compute-estimate", "relation": "measures", "target": "mlsys.kv-cache", "statement": "sizes", "conditions": "Dense attention with known layers, KV heads, head dimension, context, and precision." }, { "source": "mlsys.capacity-planning", "relation": "uses", "target": "mlsys.inference-compute-estimate", "statement": "sizes accelerators from", 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"relation": "increases", "target": "mlsys.utilization", "statement": "raises accelerator utilization by hiding input latency behind", "conditions": "Input preparation would otherwise stall compute." }, { "source": "mlsys.event-time-windowing", "relation": "part-of", "target": "mlsys.stream-processing", "statement": "is a mechanism within", "conditions": "Events carry timestamps and can arrive out of order." }, { "source": "mlsys.event-time-windowing", "relation": "determines", "target": "mlsys.feature-freshness", "statement": "sets the staleness of streaming features through", "conditions": "Window size and allowed lateness bound when an aggregate is emitted." }, { "source": "mlsys.token-streaming", "relation": "uses", "target": "mlsys.backpressure", "statement": "relies on flow control from", "conditions": "Clients consume output more slowly than the server generates it." }, { "source": "mlsys.tpot", "relation": "measures", "target": "mlsys.token-streaming", "statement": "quantifies the delivery cadence of", "conditions": "Output is returned token by token after the first." }, { "source": "mlsys.serverless-inference", "relation": "uses", "target": "mlsys.stateless-serving-function", "statement": "packages models as", "conditions": "Any instance must be able to serve any request." }, { "source": "mlsys.cold-start", "relation": "limits", "target": "mlsys.serverless-inference", "statement": "bounds the latency attainable by", "conditions": "Replicas scale from zero and must load weights before serving." }, { "source": "mlsys.llm-request-scheduling", "relation": "uses", "target": "mlsys.continuous-batching", "statement": "reorders requests inside", "conditions": "Scheduling decisions occur at each generation iteration." }, { "source": "mlsys.llm-request-scheduling", "relation": "determines", "target": "mlsys.tail-latency", "statement": "shapes", "conditions": "Long and short generations share capacity under load." }, { "source": "mlsys.containerization", "relation": "implements", "target": "mlsys.dependency-environment", "statement": "packages", "conditions": "The image pins packages, native libraries, and runtime configuration." }, { "source": "mlsys.containerization", "relation": "enables", "target": "mlsys.experiment-reproducibility", "statement": "makes reruns practical for", "conditions": "Images are versioned and run unchanged across machines." }, { "source": "mlsys.distributed-hang-diagnosis", "relation": "detects", "target": "mlsys.gray-failure", "statement": "identifies", "conditions": "A slow or stuck rank stalls collectives without crashing." }, { "source": "mlsys.distributed-hang-diagnosis", "relation": "uses", "target": "mlsys.watchdog-timer", "statement": "flags stalls with", "conditions": "Collectives and ranks are expected to make progress within a timeout." }, { "source": "mlsys.benchmark-representativeness", "relation": "requires", "target": "mlsys.workload", "statement": "depends on an accurate", "conditions": "The benchmark is used to predict deployed behavior." }, { "source": "mlsys.inference-load-scenario", "relation": "determines", "target": "mlsys.benchmark-representativeness", "statement": "sets how closely measured results match deployment for", "conditions": "Arrival pattern and latency constraint are chosen to mirror the target service." }, { "source": "mlsys.data-model-licensing", "relation": "limits", "target": "mlsys.data-acquisition", "statement": "restricts which sources can enter", "conditions": "Datasets or weights carry use, redistribution, or field-of-use terms." }, { "source": "mlsys.data-governance", "relation": "uses", "target": "mlsys.data-model-licensing", "statement": "enforces terms recorded in", "conditions": "Access and use decisions must respect license obligations." }, { "source": "mlsys.content-provenance", "relation": "uses", "target": "mlsys.artifact-signing", "statement": "relies on cryptographic signatures like", "conditions": "Provenance manifests are bound to content by signatures." }, { "source": "mlsys.risk-tiered-regulation", "relation": "requires", "target": "mlsys.content-provenance", "statement": "can mandate", "conditions": "Regulation requires machine-readable marking of synthetic content." }, { "source": "mlsys.fleet-data-engine", "relation": "uses", "target": "mlsys.active-learning", "statement": "selects uploads with", "conditions": "Triggers prioritize uncertain or rare events for labeling." }, { "source": "mlsys.fleet-data-engine", "relation": "mitigates", "target": "mlsys.distribution-shift", "statement": "counters", "conditions": "Mined data covers conditions the deployed fleet now encounters." }, { "source": "mlsys.robotics-middleware", "relation": "determines", "target": "mlsys.feedback-delay", "statement": "contributes transport and queuing delay to", "conditions": "Sensing, inference, and control run as separate communicating nodes." }, { "source": "mlsys.sensor-fusion", "relation": "uses", "target": "mlsys.robotics-middleware", 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than the user holds." }, { "source": "mlsys.graph-neural-network", "relation": "uses", "target": "mlsys.gnn-graph-partitioning", "statement": "scales training across workers with", "conditions": "The graph and its features exceed one device or machine." }, { "source": "mlsys.neighbor-sampling", "relation": "uses", "target": "mlsys.gnn-graph-partitioning", "statement": "draws mini-batches across", "conditions": "Distributed training samples neighborhoods that span partitions." }, { "source": "mlsys.host-data-loader", "relation": "part-of", "target": "mlsys.input-pipeline", "statement": "is the single-machine stage of", "conditions": "Examples are decoded and collated on the training host." }, { "source": "mlsys.host-data-loader", "relation": "determines", "target": "mlsys.utilization", "statement": "bounds accelerator utilization through", "conditions": "Per-batch preprocessing time approaches the accelerator step time." }, { "source": "mlsys.input-prefetching", "relation": "part-of", "target": "mlsys.host-data-loader", "statement": "overlaps loading with compute in", "conditions": "Worker processes prepare batches ahead of the training step." }, { "source": "mlsys.streaming-asr", "relation": "specializes", "target": "mlsys.streaming-inference", "statement": "is streaming inference over audio", "conditions": "Audio arrives in chunks and partial transcripts are emitted before the utterance ends." }, { "source": "mlsys.streaming-inference", "relation": "uses", "target": "mlsys.backpressure", "statement": "relies on", "conditions": "Input or output rates can outpace the other side of the stream." }, { "source": "mlsys.token-streaming", "relation": "specializes", "target": "mlsys.streaming-inference", "statement": "streams output", "conditions": "The request is complete but output is delivered incrementally." }, { "source": "mlsys.inference-api", "relation": "part-of", "target": "mlsys.model-server", "statement": "is the request interface of", "conditions": "Clients 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"mlsys.measurement-variability", "relation": "limits", "target": "mlsys.profiling", "statement": "confounds", "conditions": "Asynchronous kernels are timed without synchronization or warm-up." }, { "source": "mlsys.pii-deidentification", "relation": "mitigates", "target": "mlsys.privacy-leakage", "statement": "reduces the risk of", "conditions": "Identifiers are removed before training or logging." }, { "source": "mlsys.data-governance", "relation": "requires", "target": "mlsys.pii-deidentification", "statement": "requires", "conditions": "Data contains personal information subject to regulation." }, { "source": "mlsys.gradient-clipping", "relation": "mitigates", "target": "mlsys.training-instability", "statement": "suppresses", "conditions": "Instability arises from occasional very large gradients." }, { "source": "mlsys.gradient-clipping", "relation": "part-of", "target": "mlsys.optimizer", "statement": "is applied in the update step of", "conditions": "Gradients are clipped before the optimizer step." }, { "source": "mlsys.weight-initialization", "relation": "mitigates", "target": "mlsys.training-instability", "statement": "prevents early", "conditions": "Networks are deep and use rectifier or residual blocks." }, { "source": "mlsys.hyperparameter-transfer", "relation": "determines", "target": "mlsys.weight-initialization", "statement": "prescribes width-dependent scales for", "conditions": "Hyperparameters tuned on a small model are reused at a larger width." }, { "source": "mlsys.delivery-semantics", "relation": "uses", "target": "mlsys.idempotency", "statement": "achieves effectively-once results with", "conditions": "Records may be redelivered after failures." }, { "source": "mlsys.delivery-semantics", "relation": "part-of", "target": "mlsys.stream-processing", "statement": "is a guarantee of", "conditions": "The pipeline retries or replays records after failures." }, { "source": "mlsys.online-feature-consistency", "relation": "mitigates", "target": "mlsys.train-serve-skew", "statement": "prevents", "conditions": "Online and offline stores are populated by separate jobs." }, { "source": "mlsys.online-feature-consistency", "relation": "trades-off", "target": "mlsys.feature-freshness", "statement": "trades against", "conditions": "Features are updated faster than they can be materialized consistently." }, { "source": "mlsys.ood-detection", "relation": "detects", "target": "mlsys.distribution-shift", "statement": "flags individual inputs affected by", "conditions": "Inputs fall outside the training distribution." }, { "source": "mlsys.ood-detection", "relation": "enables", "target": "mlsys.selective-prediction", "statement": "supplies abstention signals to", "conditions": "The system may defer uncertain inputs." }, { "source": "mlsys.model-feature-compatibility", "relation": "limits", "target": "mlsys.rollout", "statement": "constrains", "conditions": "Model" }, { "source": "mlsys.schema-evolution", "relation": "limits", "target": "mlsys.model-feature-compatibility", "statement": "can break", "conditions": "A schema change is not mirrored in the model's feature transformations." }, { "source": "mlsys.secrets-management", "relation": "implements", "target": "mlsys.least-privilege", "statement": "enforces", "conditions": "Credentials are scoped per workload and rotated." }, { "source": "mlsys.agent-credentials", "relation": "uses", "target": "mlsys.secrets-management", "statement": "draws on", "conditions": "Agents act with credentials issued by the platform." }, { "source": "mlsys.deletion-propagation", "relation": "requires", "target": "mlsys.machine-unlearning", "statement": "extends to models through", "conditions": "Deleted data has already been used for training." }, { "source": "mlsys.deletion-propagation", "relation": "uses", "target": "mlsys.lineage", "statement": "locates copies through", "conditions": "Derived datasets and features record their sources." }, { "source": "mlsys.caching-allocator", "relation": "causes", "target": "mlsys.memory-fragmentation", "statement": "can cause", "conditions": "Tensor sizes vary across steps or requests." }, { "source": "mlsys.memory-fragmentation", "relation": "increases", "target": "mlsys.memory-footprint", "statement": "inflates reserved", "conditions": "Freed blocks cannot be reused for new allocation sizes." }, { "source": "mlsys.perception-latency-budget", "relation": "part-of", "target": "mlsys.feedback-delay", "statement": "is the sensing share of", "conditions": "Perception runs inside the control loop." }, { "source": "mlsys.perception-latency-budget", "relation": "limits", "target": "mlsys.robot-perception", "statement": "bounds model size and resolution for", "conditions": "Perception runs on fixed onboard hardware." }, { "source": "mlsys.onboard-compute-partitioning", "relation": "uses", "target": "mlsys.mixed-criticality", "statement": "respects", "conditions": "Safety-critical and best-effort tasks share hardware." }, { "source": "mlsys.onboard-compute-partitioning", "relation": "specializes", "target": "mlsys.placement", "statement": "is execution placement on a robot", "conditions": "Workloads can run on several onboard processors or be offloaded." }, { "source": "mlsys.health-checks", "relation": "enables", "target": "mlsys.load-balancing", "statement": "tells which replicas may receive traffic from", "conditions": "Replicas can be unhealthy or still loading weights." }, { "source": "mlsys.health-checks", "relation": "mitigates", "target": "mlsys.cold-start", "statement": "keeps traffic away from replicas in", "conditions": "A replica reports ready only after its model is loaded and warmed." }, { "source": "mlsys.python-gil", "relation": "increases", "target": "mlsys.dispatch", "statement": "serializes and adds to", "conditions": "Several Python threads issue work concurrently." }, { "source": "mlsys.python-gil", "relation": "limits", "target": "mlsys.host-data-loader", "statement": "pushes preprocessing into worker processes in", "conditions": "Preprocessing runs in Python" }, { "source": "mlsys.flash-endurance", "relation": "limits", "target": "mlsys.ota-model-update", "statement": "bounds the frequency of", "conditions": "Updates and logs are written to on-device flash." }, { "source": "mlsys.flash-endurance", "relation": "limits", "target": "mlsys.microcontroller", "statement": "limits the write budget of", "conditions": "The device stores weights and data in flash." }, { "source": "mlsys.intermittent-computing", "relation": "specializes", "target": "mlsys.microcontroller", "statement": "is a power regime of", "conditions": "The device has no battery and runs on harvested energy." }, { "source": "mlsys.intermittent-computing", "relation": "requires", "target": "mlsys.checkpoint", "statement": "requires fine-grained", "conditions": "Power can fail mid-inference." }, { "source": "mlsys.mmap-weights", "relation": "reduces", "target": "mlsys.memory-footprint", "statement": "removes duplicate copies from", "conditions": "Several processes serve the same read-only weights." }, { "source": "mlsys.mmap-weights", "relation": "reduces", "target": "mlsys.cold-start", "statement": "shortens", "conditions": "Only the pages touched by the first requests must load." }, { "source": "mlsys.os-memory-pressure", "relation": "limits", "target": "mlsys.on-device-memory-budget", "statement": "enforces", "conditions": "The OS kills or pages out apps that exceed their memory limit." }, { "source": "mlsys.mmap-weights", "relation": "reduces", "target": "mlsys.os-memory-pressure", "statement": "makes weight pages reclaimable under", "conditions": "File-backed pages can be dropped and reread instead of killing the app." }, { "source": "mlsys.managed-runtime-interop", "relation": "limits", "target": "mlsys.on-device-runtime", "statement": "adds copy and pause overhead to", "conditions": "The app calls native inference from a managed language." }, { "source": "mlsys.managed-runtime-interop", "relation": "increases", "target": "mlsys.tail-latency", "statement": "adds garbage-collection pauses to", "conditions": "Large buffers are allocated on the managed heap per request." }, { "source": "mlsys.zero-copy-dma", "relation": "alternative-to", "target": "mlsys.host-data-loader", "statement": "replaces CPU copying in", "conditions": "The sensor or peripheral can write into a buffer the model reads." }, { "source": "mlsys.zero-copy-dma", "relation": "reduces", "target": "mlsys.feedback-delay", "statement": "removes copy time from", "conditions": "Sensor data feeds a latency-critical loop." }, { "source": "mlsys.sensor-interference", "relation": "limits", "target": "mlsys.sensing", "statement": "corrupts measurements in", "conditions": "Several active sensors emit into the same scene." }, { "source": "mlsys.sensor-fusion", "relation": "mitigates", "target": "mlsys.sensor-interference", "statement": "can outvote corrupted readings from", "conditions": "Independent modalities observe the same scene." }, { "source": "mlsys.network-propagation-delay", "relation": "limits", "target": "mlsys.tail-latency", "statement": "sets a floor under", "conditions": "Requests cross regions or continents." }, { "source": "mlsys.network-propagation-delay", "relation": "part-of", "target": "mlsys.alpha-beta-model", "statement": "contributes to the latency term of", "conditions": "Messages travel long physical distances." }, { "source": "mlsys.cap-tradeoff", "relation": "limits", "target": "mlsys.consensus", "statement": "forces a minority partition to stop answering in", "conditions": "A partition separates replicas." }, { "source": "mlsys.cap-tradeoff", "relation": "determines", "target": "mlsys.feature-freshness", "statement": "decides whether stale values are served", "conditions": "Feature or model replicas span regions that can partition." }, { "source": "mlsys.gossip-averaging", "relation": "alternative-to", "target": "mlsys.federated-learning", "statement": "removes the central server from", "conditions": "Participants can exchange updates with peers." }, { "source": "mlsys.gossip-averaging", "relation": "alternative-to", "target": "mlsys.collectives", "statement": "replaces global collectives with neighbor exchange", "conditions": "Global synchronization is too slow or unreliable." }, { "source": "mlsys.interrupt-latency", "relation": "part-of", "target": "mlsys.wcet", "statement": "is part of the timing budget alongside", "conditions": "Inference and interrupt handlers share a processor." }, { "source": "mlsys.interrupt-latency", "relation": "increases", "target": "mlsys.feedback-delay", "statement": "adds to", "conditions": "Sensor events are delivered through interrupts." }, { "source": "mlsys.transformer-variants", "relation": "specializes", "target": "mlsys.transformer", "statement": "are arrangements of", "conditions": "Models are built from Transformer blocks." }, { "source": "mlsys.transformer-variants", "relation": "determines", "target": "mlsys.prefill-decode", "statement": "decides whether serving has separate", "conditions": "The model generates output autoregressively." }, { "source": "mlsys.data-transfer-cost", "relation": "increases", "target": "mlsys.unit-cost", "statement": "adds to", "conditions": "Data" }, { "source": "mlsys.data-transfer-cost", "relation": "part-of", "target": "mlsys.tco", "statement": "is a component of", "conditions": "Workloads span zones" }, { "source": "mlsys.duty-cycling", "relation": "uses", "target": "mlsys.idle-dynamic-power", "statement": "exploits the gap between", "conditions": "Sleep power is far below active power." }, { "source": "mlsys.duty-cycling", "relation": "reduces", "target": "mlsys.energy", "statement": "cuts average", "conditions": "Sensing and inference are needed only intermittently." }, { "source": "mlsys.priority-inversion", "relation": "limits", "target": "mlsys.real-time-scheduling", "statement": "breaks the guarantees of", "conditions": "Tasks of different priorities share a lock or resource." }, { "source": "mlsys.priority-inversion", "relation": "increases", "target": "mlsys.feedback-delay", "statement": "can unboundedly extend", "conditions": "No priority inheritance protocol is in place." }, { "source": "mlsys.randomized-smoothing", "relation": "mitigates", "target": "mlsys.evasion", "statement": "provably bounds", "conditions": "Perturbations are within the certified radius." }, { "source": "mlsys.randomized-smoothing", "relation": "alternative-to", "target": "mlsys.adversarial-training", "statement": "trades inference cost for certified guarantees against", "conditions": "A provable bound is required rather than empirical robustness." }, { "source": "mlsys.sensor-interconnect", "relation": "limits", "target": "mlsys.sensing", "statement": "bounds the data rate of", "conditions": "Sensors stream raw data into an embedded processor." }, { "source": "mlsys.zero-copy-dma", "relation": "uses", "target": "mlsys.sensor-interconnect", "statement": "moves data arriving over", "conditions": "The peripheral supports direct memory access." }, { "source": "mlsys.finite-buffer", "relation": "specializes", "target": "mlsys.queueing", "statement": "is queueing with bounded capacity", "conditions": "The buffer cannot grow without limit." }, { "source": "mlsys.backpressure", "relation": "mitigates", "target": "mlsys.finite-buffer", "statement": "prevents overflow of", "conditions": "Upstream producers can slow down when the buffer fills." }, { "source": "mlsys.ui-thread-inference", "relation": "uses", "target": "mlsys.on-device-runtime", "statement": "schedules calls into", "conditions": "The app has a main thread that must stay responsive." }, { "source": "mlsys.ui-thread-inference", "relation": "reduces", "target": "mlsys.tail-latency", "statement": "removes inference time from interactive", "conditions": "Inference takes longer than a frame interval." } ], "retired": [ { "id": "mlsys.llm-kernel-generation", "replaced_by": "mlsys.autotuning", "version": "0.2-draft", "reason": "Using ML to optimize systems is outside the taxonomy's scope; automated kernel search is covered by autotuning." } ] }