159f4588c8
Chocolatey's texlive wrapper sets ErrorActionPreference=Stop and relies on install-tl picking a random CTAN mirror at runtime. When that mirror is flaky (as mirrors.rit.edu was), the entire build fails with no fallback. Switch to calling install-tl-windows.bat directly: - Set ErrorActionPreference=Continue so we own error handling - Write a profile with instopt_adjustrepo=0 to prevent auto-mirror switching - Pass -repository explicitly, trying Illinois → MIT → mirror.ctan.org in order - Pin tlmgr repository post-install to the same stable mirror - Remove Chocolatey texlive dependency entirely
Machine Learning Systems
Principles and Practices of Engineering Artificially Intelligent Systems
Read Online | Volume I | Volume II | PDF
What This Is
The ML Systems textbook teaches you how to engineer AI systems that work in the real world. It bridges machine learning theory with systems engineering practice, covering everything from neural network fundamentals to production deployment.
This directory contains the textbook source and build system for contributors.
What You Will Learn
| ML Concepts | Systems Engineering |
|---|---|
| Neural networks and deep learning | Memory hierarchies and caching |
| Model architectures (CNNs, Transformers) | Hardware accelerators (GPUs, TPUs, NPUs) |
| Training and optimization | Distributed systems and parallelism |
| Inference and deployment | Power and thermal management |
| Compression and quantization | Latency, throughput, and efficiency |
The ML ↔ Systems Bridge
| You know... | You will learn... |
|---|---|
| How to train a model | How training scales across GPU clusters |
| That quantization shrinks models | How INT8 math maps to silicon |
| What a transformer is | Why KV-cache dominates memory |
| Models run on GPUs | How schedulers balance latency vs throughput |
| Edge devices have limits | How to co-design models and hardware |
Book Structure
This textbook is organized into two volumes following the Hennessy & Patterson pedagogical model:
| Volume | Theme | Focus |
|---|---|---|
| Volume I | Build, Optimize, Deploy | Single-machine ML systems, foundational principles |
| Volume II | Scale, Distribute, Govern | Distributed systems at production scale |
Volume I: Build, Optimize, Deploy
| Part | Focus | Chapters |
|---|---|---|
| Foundations | Core concepts | Introduction, ML Systems, DL Primer, Architectures |
| Development | Building blocks | Workflow, Data Engineering, Frameworks, Training |
| Optimization | Making it fast | Efficient AI, Optimizations, HW Acceleration, Benchmarking |
| Deployment | Making it work | Serving, MLOps, Responsible Engineering |
Volume II: Scale, Distribute, Govern
| Part | Focus | Chapters |
|---|---|---|
| Foundations of Scale | Infrastructure | Infrastructure, Storage, Communication |
| Distributed Systems | Coordination | Distributed Training, Fault Tolerance, Inference, Edge Intelligence |
| Production Challenges | Operations | On-device Learning, Privacy & Security, Robust AI, Ops at Scale |
| Responsible Deployment | Trust | Responsible AI, Sustainable AI, AI for Good, Frontiers |
What Makes This Book Different
Systems first: Start with hardware constraints and work up to algorithms, not the other way around.
Production focus: Every concept connects to real deployment scenarios, not just research benchmarks.
Open and evolving: Community-driven updates keep content current with a fast-moving field.
Hands-on companion: Pair with TinyTorch to build what you learn from scratch.
Quick Start
For Readers
# Read online
open https://mlsysbook.ai/book/
# Download formats
curl -O https://mlsysbook.ai/book/assets/downloads/Machine-Learning-Systems.pdf
curl -O https://mlsysbook.ai/book/assets/downloads/Machine-Learning-Systems.epub
For Contributors
cd book
# First time setup
./binder setup
./binder doctor
# Daily workflow
./binder clean # Clean build artifacts
./binder build # Build HTML book
./binder preview intro # Preview chapter with live reload
# Build all formats
./binder build pdf # Build PDF
./binder build epub # Build EPUB
# Utilities
./binder help # Show all commands
./binder list # List chapters
./binder validate all # Run Binder-native validation checks
./binder maintain repo-health # Run Binder-native repo diagnostics
Directory Structure
book/
├── quarto/ # Book source (Quarto markdown)
│ ├── contents/ # Chapter content
│ │ ├── vol1/ # Volume I: Build, Optimize, Deploy
│ │ ├── vol2/ # Volume II: Scale, Distribute, Govern
│ │ ├── frontmatter/ # Preface, about, changelog
│ │ └── backmatter/ # References, glossary
│ ├── assets/ # Images, downloads
│ └── config/ # Quarto configuration files
├── cli/ # Binder CLI tool
├── docker/ # Development containers
├── docs/ # Documentation
├── tools/ # Build scripts
└── binder # CLI entry point
Documentation
| Audience | Resources |
|---|---|
| Readers | Online Book ・ Volume I ・ Volume II ・ PDF |
| Contributors | CONTRIBUTING.md ・ BUILD.md |
| Developers | DEVELOPMENT.md ・ BINDER.md |
Binder is the public automation API for book build/validate/maintenance workflows. Use Binder subcommands in editor integrations and CI where possible.
Contributing
We welcome contributions! See docs/CONTRIBUTING.md for guidelines.
- Fork and clone the repository
- Set up your environment:
./binder setup - Find an issue or propose a change
- Make your changes in the
quarto/contents/directory - Preview your changes:
./binder preview <chapter> - Submit a PR with a clear description
Related
| Component | Description |
|---|---|
| Main README | Project overview and ecosystem |
| TinyTorch | Build ML frameworks from scratch |
| Hardware Kits | Deploy to Arduino, Raspberry Pi, edge devices |
| Website | Read the book online |
Contributors
Thanks to these wonderful people who helped improve the book!
Legend: 🪲 Bug Hunter · ⚡ Code Warrior · 📚 Documentation Hero · 🎨 Design Artist · 🧠 Idea Generator · 🔎 Code Reviewer · 🧪 Test Engineer · 🛠️ Tool Builder
Recognize a contributor: Comment on any issue or PR:
@all-contributors please add @username for doc, review, translation, or design
License
Book content is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
See LICENSE.md for details.