---
**正文**
Machine Learning Systems
Principles and Practices of Engineering Artificially Intelligent Systems
English
•
中文
•
日本�
•
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
Component
Role in the Curriculum
Link
📖
Textbook
Two-volume MIT Press textbook. The theory, the mental models, and the quantitative reasoning that everything else builds on.
Vol I
·
Vol II
🔬
Labs
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.
Launch labs
·
Repo guide
🔥
Tiny🔥Torch
Build your own ML framework from scratch across 20 progressive modules. You don't understand a system until you've built one.
Get started
🛠�
Hardware Kits
Deploy ML to Arduino, Seeed, Grove, and Raspberry Pi devices. Real memory limits, real power budgets, real latency.
Browse labs
🔮
MLSys·im
Calculate

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🔗 **[harvard-edge/cs249r_book （今日⭐329颗星）](https://github.com/harvard-edge/cs249r_book)**
> harvard-edge/cs249r_book (⭐ 329 stars today)
🏷️ 来源: GitHub Trending

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🐾 **小九锐评**

开源项目是学东西最快的途径。这个项目值得关注，代码和文档质量都不错。

_转载自 GitHub Trending，内容版权归原作者所有_
⏱️ 2026-07-06 18:30