Principles and Practices of Engineering Artificially Intelligent Systems
📖 Read Online • 🔥 TinyTorch • 📄 Download PDF • 📓 Download EPUB • 🌐 Explore Ecosystem
📚 Hardcopy edition coming 2026 with MIT Press.
This repository contains an open source textbook and hands-on framework 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.
Read → Experiment → Build
| Component | Description | Getting Started |
|---|---|---|
| 📖 Book | The Machine Learning Systems textbook that walks through design, optimization, deployment, edge computing, and responsible AI in a clear, end-to-end way | book/README.md |
| 🔥 TinyTorch | A from-scratch ML framework that lets you build the full stack yourself, piece by piece, and learn how real systems work under the hood | tinytorch/README.md |
| 🔮 Lens | Hands-on Colab simulations that let you experiment with ML systems, explore tradeoffs, and see how design decisions ripple through a working microchamber | Coming in Summer 2026 |
This textbook teaches you to think at the intersection of machine learning and systems engineering. Each chapter bridges algorithmic concepts with the infrastructure that makes them work in practice.
| ML Concept | Systems Concept | What You Learn |
|---|---|---|
| Model parameters | Memory constraints | How to fit large models on resource-limited devices |
| Inference latency | Hardware acceleration | How GPUs, TPUs, and accelerators execute neural networks |
| Training convergence | Compute efficiency | How mixed-precision and optimization techniques reduce cost |
| Model accuracy | Quantization and pruning | How to compress models while preserving performance |
| Data requirements | Pipeline infrastructure | How to build efficient data loading and preprocessing |
| Model deployment | MLOps practices | How to monitor, version, and update models in production |
| Privacy constraints | On-device learning | How to train and adapt models without sending data to the cloud |
| Part | Focus | Chapters |
|---|---|---|
| I. Foundations | Core concepts | Introduction, ML Systems, DL Primer, Architectures |
| II. Design | Building blocks | Workflow, Data Engineering, Frameworks, Training |
| III. Performance | Making it fast | Efficient AI, Optimizations, HW Acceleration, Benchmarking |
| IV. Deployment | Making it work | MLOps, On-device Learning, Privacy, Robustness |
| V. Trust | Making it right | Responsible AI, Sustainable AI, AI for Good |
| VI. Frontiers | What's next | Emerging trends and future directions |
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.
Students learn how to train ML models, but few are taught how to engineer the systems that make those models useful in practice. As AI capabilities grow, progress will depend less on developing new algorithms and more on developing the engineers who can design scalable, efficient, and responsible systems that bring those algorithms to life.
This project 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
For Readers:
- Read Chapter 1 and the overview
- Skim the Benchmarking chapter to know what to measure
- Pick a TinyML kit and run a lab
For Builders:
- Clone this repo and explore TinyTorch
- Start with Module 01 (Tensors) and build your way up
- Train a CNN on CIFAR-10 with code you wrote from scratch
For Everyone:
- Say hello in Introduce Yourself. I will do my best to reply.
We are tracking our way to 1 million learners by 2030. Every star, share, and contribution helps us get there.
What gets measured gets done. Every star represents a learner or believer in accessible AI education.
1 ✓ → 10 ✓ → 100 ✓ → 1,000 ✓ → 10,000 ✓ → 100,000 by 2026 → ... → 1M by 2030
All contributions go to Open Collective, a transparent fund that supports educational outreach: TinyML system kits, workshops, and infrastructure for learners worldwide.
| Resource | Description |
|---|---|
| 📖 Textbook | Interactive online textbook |
| 🔥 TinyTorch | Educational ML framework (Early Access) |
| 🌐 Ecosystem | Resources, workshops, and community |
| 💬 Discussions | Questions and ideas |
We welcome contributions to both the book and TinyTorch!
| I want to... | Go here |
|---|---|
| Fix a typo or improve a chapter | book/docs/CONTRIBUTING.md |
| Add a TinyTorch module or fix a bug | tinytorch/CONTRIBUTING.md |
| Report an issue | GitHub Issues |
| Ask a question | GitHub Discussions |
# Read online
open https://mlsysbook.ai
# Download PDF
curl -O https://mlsysbook.ai/pdf
# Download EPUB
curl -O https://mlsysbook.ai/epubcd book
# First time setup
./binder setup
./binder doctor
# Daily workflow
./binder clean
./binder build
./binder preview introcd tinytorch
# One-command setup
tito setup
source .venv/bin/activate
# Start building
tito module start 01See book/README.md and tinytorch/README.md for detailed instructions.
@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}
}- Book content: Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0)
- TinyTorch code: MIT License
Thanks goes to these wonderful people who have contributed to making this resource better for everyone:
⭐ Star us on GitHub • 💬 Join discussions • 🌐 Visit mlsysbook.ai
Built with dedication by the MLSysBook community.
![@github-actions[bot]](https://avatars.githubusercontent.com/in/15368?s=64&v=4){kind=small}
