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Add infrastructure for publishing Volume I and Volume II as standalone sites: Volume Configs: - Add _quarto-html-vol1.yml, _quarto-pdf-vol1.yml, _quarto-epub-vol1.yml - Add _quarto-html-vol2.yml, _quarto-pdf-vol2.yml, _quarto-epub-vol2.yml - Update navbar cross-links in all configs (book, kits, labs) Volume Content: - Add vol1/index.qmd, vol1/frontmatter/, vol1/backmatter/ - Add vol2/index.qmd, vol2/frontmatter/, vol2/backmatter/ Landing Page: - Create landing/index.html with AI Engineering branding - Add navigation to all resources (Vol I, Vol II, Full, TinyTorch, Kits) Workflows: - Add build_target input to book-build-container.yml (combined/vol1/vol2/all) - Add deploy_target input to book-publish-live.yml - Add matrix entries for volume-specific builds (Linux) - Update deployment to handle /book/, /vol1/, /vol2/, and root CLI: - Update binder CLI to support --vol1 and --vol2 flags Documentation: - Update README.md with volume links and new URL structure - Update book/README.md with correct paths URL Structure: - mlsysbook.ai/ -> Landing page - mlsysbook.ai/book/ -> Full textbook - mlsysbook.ai/vol1/ -> Volume I standalone - mlsysbook.ai/vol2/ -> Volume II standalone
Labs
Understanding the Interplay Between Algorithms and Systems
Status: Coming Summer 2026
What Are Labs?
Labs are hands-on interactive notebooks that bridge the gap between reading about ML systems (the textbook) and building them from scratch (TinyTorch).
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Textbook │────▶│ Labs │────▶│ TinyTorch │
│ │ │ │ │ │
│ Concepts & │ │ Experiment & │ │ Build from │
│ Theory │ │ Explore │ │ Scratch │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
READ EXPLORE BUILD
The Learning Journey
| Phase | Resource | What You Do |
|---|---|---|
| Understand | Textbook | Learn concepts, theory, and system design principles |
| Experiment | Labs | Explore tradeoffs, tweak parameters, see how decisions ripple through systems |
| Build | TinyTorch | Implement everything from scratch, own every line of code |
Why Labs?
ML systems are where algorithms meet hardware. A model that works perfectly in theory can fail in practice due to memory limits, latency constraints, or numerical precision. Labs help you develop intuition for these algorithm-system interactions.
- See the tradeoffs — How does batch size affect memory? How does quantization affect accuracy?
- Explore interactively — Adjust parameters and watch how changes ripple through the system
- Build intuition — Understand why systems behave the way they do, not just what they do
- Zero setup — Run directly in your browser via Google Colab
Example Topics (Planned)
- Memory vs. Compute Tradeoffs — Watch how batch size affects memory footprint and training speed
- Quantization Effects — See accuracy degradation as you reduce precision from FP32 → INT8 → INT4
- Attention Visualization — Explore what transformer attention heads actually learn
- Optimization Landscapes — Navigate loss surfaces with different optimizers
- Pruning Strategies — Compare structured vs. unstructured pruning on real models
Stay Updated
Labs are under active development. To be notified when they launch:
Related Resources
| Resource | Description |
|---|---|
| Textbook | ML Systems principles and practices |
| TinyTorch | Build your own ML framework from scratch |
| Discussions | Ask questions, share feedback |
Read. Explore. Build. (Labs coming soon)