Customization Guide

Adapt the curriculum to your program’s format and emphasis

The 16-week syllabi are designed as complete, ready-to-use courses. But not every program has 16 weeks, and not every audience has the same emphasis. This guide shows how to adapt.

10-Week Quarter Version (Foundations)

For quarter systems, compress the 16-week Foundations syllabus:

Quarter Week	Content (from 16-week)	What Changes
1	Weeks 1–2	Introduction + ML Systems combined; Module 01 only
2	Week 3	ML Workflow; Module 02 starts
3	Weeks 4–5	Data Engineering + Neural Computation; Module 02 + 03
4	Week 6	NN Architectures; Module 04
5	Weeks 7–8	Frameworks + Training; Module 05 + 06
6	Week 9	Data Selection; Module 07
7	Week 10	Model Compression + Lab 09 (Quantization); Module 08
8	Week 11	HW Acceleration + Lab 10 (Roofline)
9	Weeks 13–14	Serving + Operations; Labs 12–13
10	Week 16	Capstone (AI Olympics, reduced scope)

What gets dropped: Benchmarking (Week 12), Responsible Engineering (Week 15) — assign as optional reading. Integrate key responsibility points into the capstone rubric.

What gets compressed: TinyTorch Modules 01+02 doubled up in Weeks 1+3. Labs 00, 01, and 03 become optional.

Quarter Tradeoff

The 10-week version sacrifices breathing room. Consider reducing Decision Logs to 100 words and assigning only 2 Design Challenges instead of 4.

3-Day Workshop Version

For short workshops with experienced practitioners:

Day	Focus	Materials
Day 1	The Physics of Inference	Iron Law introduction + Labs 01, 05, 09 (Magnitude Gap, Architecture Tradeoffs, Quantization)
Day 2	The Optimization Frontier	Labs 10, 11 (Roofline, Benchmarking) + TinyTorch Module 08 speed-run
Day 3	Production Deployment	Labs 12, 13 (Tail Latency, Drift Detection) + mini Design Challenge

Focus exclusively on the Iron Law and Interactive Labs. Skip TinyTorch (except as demo). No formal assessment — use labs for hands-on discovery.

For Software Engineers

If your audience is experienced developers, lean into TinyTorch:

Weeks	Focus	Modules
1–4	Building the Autograd Engine	Modules 01–06 (Tensor → Autograd)
5–8	From CNNs to Transformers	Modules 09–13 (Conv → Transformer)
9–12	Production Optimization	Modules 14–19 (Profiling → Benchmarking)
13–16	Capstone: Torch Olympics	Module 20 + competition

Use textbook chapters as background reading, not lecture material. Labs serve as validation checkpoints, not primary pedagogy.

For Computer Architects

Shift the focus toward Hardware Acceleration and mlsysim:

Use the hardware Zoo in mlsysim to compare architectures (H100, B200, edge devices)
Spend 2 weeks on the Roofline model — have students plot multiple workloads
Extend model compression to 2 weeks (quantization + pruning as hardware-aware optimizations)
Use hardware kits extensively — make them mandatory, not optional
Reduce TinyTorch to Modules 01–03 (enough to understand what frameworks do)

Graduate Seminar Version

For a graduate-level seminar (assumes strong systems background):

Week	Topic	Textbook	Paper
1	The Iron Law	Vol I: Intro + ML Systems	Hennessy & Patterson, “A New Golden Age” (2019)
2	Memory Hierarchy	Vol I: HW Acceleration	Williams et al., “Roofline” (2009)
3	Quantization	Vol I: Model Compression	Dettmers et al., “LLM.int8()” (2022)
4	Serving Systems	Vol I: Model Serving	Yu et al., “Orca” (2022)
5	Distributed Training	Vol II: Distributed Training	Shoeybi et al., “Megatron-LM” (2020)
6	3D Parallelism	Vol II: Distributed Training	Narayanan et al., “Efficient Large-Scale Training” (2021)
7	Collective Comms	Vol II: Collective Comm.	Patarasuk & Yuan, “Bandwidth Optimal All-Reduce” (2009)
8	Fault Tolerance	Vol II: Fault Tolerance	Jeon et al., “Large-Scale GPU Clusters” (2019)
9	Inference at Scale	Vol II: Inference	Kwon et al., “vLLM/PagedAttention” (2023)
10	KV-Cache Optimization	Vol II: Inference	Ainslie et al., “GQA” (2023)
11	Edge Intelligence	Vol II: Edge Intelligence	Lin et al., “MCUNet” (2020)
12	Fleet Operations	Vol II: Ops at Scale	Zhao et al., “ATC’24 Fleet Analysis” (2024)
13	Sustainability	Vol II: Sustainable AI	Patterson et al., “Carbon Emissions and AI” (2021)
14	Student presentations	—	—

Assessment: 40% paper presentations, 30% lab Decision Logs (selected labs only), 30% semester project (original system design or benchmarking study).

Mixing and Matching Components

Each component is independently adoptable:

Pattern	Components Used	Typical Context
Textbook Only	Vol I or II as required reading	Supplement for existing ML course
Textbook + Labs	Readings + interactive labs	Active learning without coding assignments
TinyTorch Only	20 modules as programming assignments	Systems programming course
Labs Only	Interactive labs as in-class activities	Active learning supplement for any course
Hardware Kits Only	Edge deployment labs	Embedded systems course
Full Stack	All components integrated	Dedicated ML Systems course

Start Small, Layer Up

If adopting for the first time, start with Textbook + Labs for one semester. Add TinyTorch the second time you teach it. Add hardware kits the third. Each component is valuable on its own.