GitHub – harvard-edge/cs249r_book: Introduction to Machine Learning Systems Skip to content You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert harvard-edge / cs249r_book Public Notifications You must be signed in to change notification settings Fork 2.3k Star 19.9k Introduction to Machine Learning Systems mlsysbook.ai/ License View license 19.9k stars 2.3k forks Branches Tags Activity Star Notifications You must be signed in to change notification settings harvard-edge/cs249r_book dev Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit History 9,525 Commits 9,525 Commits .github .github .vale/ styles/ textbook .vale/ styles/ textbook README README _brand _brand binder binder book book kits kits labs labs tinytorch tinytorch .all-contributorsrc .all-contributorsrc .codespell-ignore-words.txt .codespell-ignore-words.txt .envrc .envrc .gitignore .gitignore .nojekyll .nojekyll .pre-commit-config.yaml .pre-commit-config.yaml .yamllint .yamllint CITATION.bib CITATION.bib CNAME CNAME LICENSE.md LICENSE.md README.md README.md pyproject.toml pyproject.toml requirements.txt requirements.txt View all files Repository files navigation Machine Learning Systems Principles and Practices of Engineering Artificially Intelligent Systems English • 中文 • 日本語 • 한국어 📖 Read Online • Tiny🔥Torch • 📄 Download PDF • 📓 Download EPUB • 🌐 Explore Ecosystem 📚 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: 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. The long term impact of AI will be shaped by engineers who can turn ideas into working, dependable systems. What’s in this repo This repository is the open learning stack for AI systems engineering. It includes the textbook source, TinyTorch, hardware kits, and upcoming co-labs that connect principles to runnable code and real devices. Start Here Choose a path based on your goal. READ Start with the textbook . Try Chapter 1 and the Benchmarking chapter . BUILD Start TinyTorch with the getting started guide . Begin with Module 01 and work up from CNNs to transformers and the MLPerf benchmarks. DEPLOY Pick a hardware kit and run the labs on Arduino, Raspberry Pi, and other edge devices. CONNECT Say hello in Discussions . We will do our best to reply. The Learning Stack The learning stack below shows how the textbook connects to hands

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