harvard-edge/cs249r_book
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
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README & documentation
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Principles and Practices of Engineering Artificially Intelligent Systems English • 中文 • 日本語 • 한국어 📘 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. 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. I designed this as a single integrated curriculum,…
Read the full README ↗ · Preview checked 2026-10-27T01:30:18.582Z
Inside the original README — Document outline
- Machine Learning Systems
- Mission
- Why One Repository
- The Curriculum
- For Students
- For Educators
- Choose Your Path
- Adjacent and Experimental Work
- What You Will Learn
- Book Structure
- How This Curriculum Is Built and Verified
- How the Volumes Connect
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What this repository does
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
Repository facts
- Owner
- harvard-edge
- Primary language
- Python
- Stars
- 29,747
- Forks
- 3,680
- Open issues + pull requests
- 8
- License
- Other
- Archived
- No
- Default branch
- dev
- Created
- 2023-09-06T19:31:06.000Z
- Last push
- 2026-10-31T12:28:34.000Z
Topics and intended use
Owner-supplied topics: agentic-ai, ai-agents, artificial-intelligence, cloud-ml, computer-systems, courseware, deep-learning, edge-machine-learning, embedded-ml, embodied-ai, machine-learning, machine-learning-systems, mlsys, mobile-ml, physical-ai, robotics, textbook, tinyml
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