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harvard-edge
Introduction to Machine Learning Systems
Machine Learning Systems
Principles and Practices of Engineering Artificially Intelligent Systems
📖 Read Online • 📄 Download PDF • 📓 Download EPUB • 🌐 Explore Ecosystem
📚 Hardcopy edition coming 2026 with MIT Press.
About This Book
The open source textbook for learning how to engineer AI systems. It began in Harvard’s CS249r course by Prof. Vijay Janapa Reddi. Today, it supports classrooms, study groups, and independent learners around the world.
Mission: Accessible AI systems education for anyone, anywhere. One chapter at a time.
Why This Book Exists
Many students learn how to train ML models but not how to build and engineer the systems that make those models useful in the real world. As AI becomes more capable, the real bottleneck will not just be algorithms, but engineers who can design efficient, scalable, and sustainable systems that put those algorithms to work responsibly.
This book is part of a broader personal mission to educate one million learners worldwide in the foundations of AI systems engineering. The long term impact of AI will be shaped by a generation of engineers and builders who know how to turn ideas into working systems.
— Vijay Janapa Reddi
What Makes This Book Different
This project is a living textbook. I keep it updated as the field grows, with community input along the way.
AI may feel like it is moving at lightning speed, but the engineering building blocks that make it work do not change as quickly as the headlines. This book is built around those stable foundations.
Think of it like LEGO. New sets arrive all the time, but the bricks themselves stay the same. Once you learn how the bricks fit together, you can build anything. Here, those “AI bricks” are the solid systems principles that make AI work.
Whether you are reading a chapter, running a lab, or sharing feedback, you are helping make these ideas more accessible to the next learner.
Thank you for being a part of the story 🙏
Start Here
- Read Chapter 1 and the overview.
- Skim the Benchmarking chapter to know what to measure.
- Pick a TinyML kit and run a lab.
- Say hello in Introduce Yourself. I will do my best to reply.
📚 What You Will Learn
This textbook gives you a systems level understanding of machine learning, bridging the gap between algorithms and the real world infrastructure that makes them work. You will learn how to design, build, and reason about the components that make modern AI possible.
| Topic | What You Will Learn |
|---|---|
| System Design | How to design and structure end-to-end ML systems that are scalable, modular, and maintainable |
| Data Engineering | How to build reliable pipelines for collection, labeling, and processing |
| Model Deployment | How to turn trained models into robust, production-ready services |
| MLOps and Monitoring | How to operate, monitor, and sustain AI systems over time |
| Edge and Embedded AI | How to deploy ML on mobile, embedded, and resource-constrained devices |
| Responsible and Sustainable AI | How to design systems with privacy, security, and environmental impact in mind |
⭐ Support This Work
Show Support
Star the repository. It signals interest and helps us secure resources for open education.
Fund the Mission
Your support helps provide TinyML kits, workshops, and infrastructure for learners worldwide.
🌐 Community and Resources
| Resource | Description |
|---|---|
| 📚 Main Site | Course materials, labs, and updates |
| 🔥 TinyTorch | Educational ML framework (🚧 Work in progress) |
| 💬 Discussions/Community | Questions and ideas |
🛠️ Contributing
Want to improve this resource? We welcome contributions!
- Contribution guide
- Development setup
- Community discussions (see Community Resources above)
🚀 Quick Start
For Readers
# Read online
open https://mlsysbook.ai
# Download PDF
curl -O https://mlsysbook.ai/pdf
# Download EPUB
curl -O https://mlsysbook.ai/epub
For Contributors
git clone https://github.com/harvard-edge/cs249r_book.git
cd cs249r_book
# Quick setup
./binder setup
./binder doctor
# Fast iteration
./binder preview intro
./binder build intro
./binder html intro
./binder pdf intro
./binder epub intro
# Build the whole book
./binder build
./binder html
./binder pdf
./binder epub
# Utilities
./binder help
./binder list
./binder status
📋 Citation & License
Citation
@inproceedings{reddi2024mlsysbook,
title = {MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering},
author = {Reddi, Vijay Janapa},
booktitle = {2024 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS)},
pages = {41--42},
year = {2024},
organization = {IEEE},
url = {https://mlsysbook.org}
}
License
This work is licensed under Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0). You may share and adapt the material for non-commercial purposes with appropriate credit.
🙏 Contributors
Thanks goes to these wonderful people who have contributed to making this resource better for everyone:
Made with ❤️ for AI learners worldwide
Our goal is to educate 1 million AI systems engineers for the future at the edge of AI.
harvard-edge