Lecture Slides
35 Beamer decks, TinyML courseware, speaker notes, active learning, and 266 original SVG diagrams. Free to download, customize, and teach from.
Vol I · Ch 1: Title Slide — Custom Beamer Theme
Vol I · Ch 1: The Iron Law — Focus Slides for Key Equations
Vol I · Ch 11: The Roofline Model — Original SVG Diagrams
Vol II · Ch 2: Three Walls — Quantitative Systems Tables
Vol II · Ch 3: RDMA & GPUDirect — Semantic Color Diagrams
Vol II · Ch 7: Optimal Checkpointing — Math + Visualization
Vol I · Ch 1: The Degradation Equation — Every Slide Has Speaker Notes
Preview of Beamer slides (Vol I & II) — 7 of 1,099 shown. Every slide includes speaker notes and timing guidance.
Introduction to Machine Learning Systems
17 decks covering the full single-machine ML stack: data engineering, neural computation, architectures, frameworks, training, compression, hardware, serving, and operations.
Volume IIMachine Learning Systems at Scale
18 decks covering distributed infrastructure: compute clusters, network fabrics, distributed training, fault tolerance, fleet orchestration, inference at scale, and governance.
TinyMLTiny Machine Learning
Complete edX courseware: ML fundamentals, keyword spotting, visual wake words, anomaly detection, embedded deployment on Arduino, and MLOps at scale.
| Speaker Notes | Every slide has timing estimates, teaching guidance, common student errors, and discussion prompts. | Guide → |
| Active Learning | 5+ exercises per deck: predict, calculate, discuss, peer instruction, retrieval practice, muddiest point. | Details → |
| SVG Diagrams | 266 original vector diagrams with semantic colors. Editable source files — not locked images. | Source → |
| PowerPoint | Every deck available as PPTX for presenter mode and annotations. Image-based (not editable). | Download → |
| Beamer Source | Full LaTeX source with custom theme. Fork, customize, rebuild with make. |
Customize → |
| Semester Plans | 16-week schedules for Vol I, Vol II, a combined 32-week plan, and a 10–12 week TinyML syllabus. | Plans → |
| Textbook | The slides are derived from this two-volume open textbook. Read online or download PDF. | Vol I · Vol II |
| Instructor Blueprint | Syllabi, assessment rubrics, TA guide, and course customization — the full teaching toolkit. | Blueprint → |
| Interactive Labs | 33 browser-based labs powered by MLSys·im. Pair with slides for hands-on learning. | Labs → |
| Hardware Kits | Arduino, Raspberry Pi, and Seeed deployment labs for TinyML and edge AI. | Kits → |
Part of the MLSysBook Ecosystem
Textbook
Comprehensive theory across the full ML systems stack.
Lecture Slides
Beamer decks and teaching materials for every chapter.
Labs
Interactive Marimo notebooks that measure the book's claims.
Hardware Kits
Hands-on embedded ML deployment on real devices.
Instructor Hub
Course maps, syllabi, and adoption resources for teaching.
TinyTorch
Build your own ML framework from scratch, module by module.
MLSysIM
The physics engine behind every quantitative figure in the book.