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Machine Learning Systems

Principles and Practices of Engineering Artificially Intelligent Systems

Build Website PDF EPUB

Read Online | PDF | EPUB


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

Part Focus Chapters
Foundations ML and systems basics Introduction, ML Primer, DL Primer, AI Acceleration
Workflow Production pipeline Workflows, Data Engineering, Frameworks
Training Learning at scale Training, Distributed Training, Efficient AI
Deployment Real-world systems Inference, On-Device AI, Hardware Benchmarking, Ops
Advanced Frontier topics Privacy, Security, Responsible AI, Sustainable AI, Genertic AI, 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

# Download formats
curl -O https://mlsysbook.ai/pdf
curl -O https://mlsysbook.ai/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 pdf                # Build PDF
./binder epub               # Build EPUB

# Utilities
./binder help               # Show all commands
./binder list               # List chapters

Directory Structure

book/
├── quarto/              # Book source (Quarto markdown)
│   ├── contents/        # Chapter content
│   │   ├── core/        # Core chapters
│   │   ├── labs/        # Hands-on labs
│   │   ├── frontmatter/ # Preface, about, changelog
│   │   └── backmatter/  # References, glossary
│   ├── assets/          # Images, downloads
│   └── _quarto.yml      # Quarto configuration
├── cli/                 # Binder CLI tool
├── docker/              # Development containers
├── docs/                # Documentation
├── tools/               # Build scripts
└── binder               # CLI entry point

Documentation

Audience Resources
Readers Online BookPDFEPUB
Contributors CONTRIBUTING.mdBUILD.md
Developers DEVELOPMENT.mdBINDER.md

Contributing

We welcome contributions! See docs/CONTRIBUTING.md for guidelines.

  1. Fork and clone the repository
  2. Set up your environment: ./binder setup
  3. Find an issue or propose a change
  4. Make your changes in the quarto/contents/ directory
  5. Preview your changes: ./binder preview <chapter>
  6. Submit a PR with a clear description

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

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.