Machine Learning Systems

Principles and Practices of Engineering Artificially Intelligent Systems

English • 中文 • 日本語 • 한국어

📘 Textbook • 📗 Vol I + 📘 Vol II • 🔥 TinyTorch • 🔬 Labs • 🔮 MLSys·im • 💼 StaffML

📚 Hardcopy edition coming 2026 with MIT Press.

Mission

The world is rushing to build AI systems. It is not engineering them.

That gap is what we mean by AI engineering.

AI engineering is the discipline of building efficient, reliable, safe, and robust intelligent systems that operate in the real world, not just models in isolation. Our mission is to establish AI engineering as a foundational discipline alongside software engineering and computer engineering, by teaching how to design, build, and evaluate end-to-end intelligent systems.

Our goal: Help 100,000 learners master ML Systems this year, and reach 1 million by 2030.

Why One Repository

I designed this as a single integrated curriculum, not a collection of independent projects. The textbook teaches the theory. TinyTorch makes you build the internals. The hardware kits force you to confront real constraints. The simulator lets you reason about infrastructure you can't afford to rent. Each piece exists because I found that students who only read don't internalize, and students who only code don't generalize.

The repository is the curriculum.

A growing community of contributors helps improve every part of it: fixing errors, sharpening explanations, testing on new hardware. Their work makes this better for everyone, and I'm grateful for every pull request.

The Curriculum

Every component connects. The textbook gives you the mental models. The labs let you reason through trade-offs interactively, powered by MLSys·im — a modeling engine for infrastructure you can't physically access, and a standalone tool in its own right. TinyTorch makes you build the machinery yourself. The hardware kits put you face-to-face with real deployment constraints. StaffML tests whether you actually understand it. Socratiq adds AI-guided reading, contextual quizzes, and spaced repetition inside the learning experience. And the instructor hub, slides, and newsletter give educators everything they need to bring this into a classroom.

For Students

For Educators

Choose Your Path

The pieces are designed to work together, but you do not need to adopt everything at once.

The learning loop is: Read → Explore → Build → Model → Deploy → Practice → Teach.

Adjacent and Experimental Work

Some projects are intentionally earlier-stage than the main curriculum:

• Socratiq explores AI-guided reading, contextual quizzes, and spaced repetition for static learning sites.
• MLPerf EDU is an under-construction pedagogical benchmark suite aligned with MLCommons MLPerf.
• ML Systems Design Grammar is an experimental framework for reasoning from stable primitives, constraints, and rewrite rules.

What You Will Learn

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.

Book Structure

The textbook follows the Hennessy & Patterson pedagogical model across two volumes:

FAQ

Who is this for, and what should I know first?

This is for anyone who wants to engineer intelligent systems, not only train models: students, working engineers moving into ML infrastructure, and educators building a course. We assume you can program in Python and have met basic machine learning ideas, but the book builds the systems concepts from the ground up. You do not need a background in computer architecture, distributed systems, or datacenter ope