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哈佛开源 ML Systems 教材：构建 AI 工程体系
哈佛大学将 CS249r《Machine Learning Systems》课程教材完全开源，旨在确立“AI 工程”作为独立学科。该资源包含理论书籍、TinyTorch 实现库及硬件套件，强调从模型训练到系统部署的全链路能力。目前纸质版预计 2026 年由 MIT Press 出版。
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本文目录4 个章节
Machine Learning Systems
Principles and Practices of Engineering Artificially Intelligent Systems
中文 •
📘 Textbook Series • 📗 Vol I • 📘 Vol II (Preview) • 🟣 Vol III (In Dev) • 🌲 Vol IV (In Dev) 🔥 TinyTorch • 🔬 Labs • 🧰 Kits • 🔮 MLSys·im • 🎓 Instructors • 💼 StaffML
📚 Hardcopy edition coming 2026 with MIT Press.
</div>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
<table width="100%" style="width:100%"> <thead> <tr> <th width="5%"></th> <th width="15%">Component</th> <th width="50%">Role in the Curriculum</th> <th width="30%">Link</th> </tr> </thead> <tbody> <tr> <td align="center">📖</td> <td><b>Textbook Series</b></td> <td>Four-volume MIT Press textbook series on Machine Learning Systems Architecture: <br>• <a href="https://mlsysbook.ai/vol1/"><b>Vol I: Introduction to Machine Learning Systems</b></a> <i>(Released)</i><br> The foundations, abstractions, lifecycle, data, training, inference, evaluation, deployment, and responsible systems. <br>• <a href="https://mlsysbook.ai/vol2/"><b>Vol II: Scaling Machine Learning Systems</b></a> <i>(Preview)</i><br> Distributed computation, accelerators, memory, communication, parallelism, serving, reliability, and efficiency. <br>• <a href="books/vol3/"><b>Vol III: Agentic Machine Learning Systems</b></a> <i>(In Development)</i><br> Reasoning and acting loops, memory, tools, planning, search, orchestration, evaluation, security, and multi-agent systems. <br>• <a href="books/vol4/"><b>Vol IV: Physical AI Systems</b></a> <i>(In Development)</i><br> Sensing, perception, world models, control, robotics, embodiment, real-time constraints, safety, and hardware. <br><br>⚠️ <i>Volumes III and IV are in development and change quickly as I iterate. Please do not cite or teach from them yet. Feedback is welcome through the <a href="https://github.com/harvard-edge/cs249r_book/issues/new/choose">Book feedback</a> issue forms.</i> </td> <td><a href="https://mlsysbook.ai/vol1/">Vol I</a> · <a href="https://mlsysbook.ai/vol2/">Vol II (Preview)</a> · <a href="books/vol3/">Vol III (In Dev)</a> · <a href="books/vol4/">Vol IV (In Dev)</a></td> </tr> <tr> <td align="center">🔬</td> <td><b>Labs</b></td> <td>Interactive Marimo notebooks where you explore trade-offs from the textbook: change a parameter, see what breaks, build intuition. Powered by MLSys·im under the hood.</td> <td><a href="https://mlsysbook.ai/labs/">Launch labs</a> · <a href="labs/README.md">Repo guide</a></td> </tr> <tr> <td align="center">🔥</td> <td><b>Tiny🔥Torch</b></td> <td>Build your own ML framework from scratch across 20 progressive modules. You don't understand a system until you've built one.</td> <td><a href="https://mlsysbook.ai/tinytorch/">Get started</a></td> </tr> <tr> <td align="center">🛠️</td> <td><b>Hardware Kits</b></td> <td>Deploy ML to Arduino, Seeed, Grove, and Raspberry Pi devices. Real memory limits, real power budgets, real latency.</td> <td><a href="https://mlsysbook.ai/kits">Browse labs</a></td> </tr> <tr> <td align="center">🔮</td> <td><b>MLSys·im</b></td> <td>Calculate memory bottlenecks, network saturation, and scheduling limits at infrastructure scales you can't physically access.</td> <td><a href="https://mlsysbook.ai/mlsysim/">Use simulator</a> · <a href="mlsysim/README.md">Repo guide</a></td> </tr> <tr> <td align="center">💼</td> <td><b>StaffML</b></td> <td>Physics-grounded interview questions for ML systems roles. Vault, practice drills, mock interviews, and progress tracking.</td> <td><a href="https://mlsysbook.ai/staffml/">Practice</a> · <a href="staffml/README.md">Repo guide</a></td> </tr> </tbody> </table>（注：更多技术实现细节与完整 API 文档请查阅原项目 README）