{"slug": "machine-learning-systems", "title": "Machine Learning Systems", "summary": "Harvard University and MIT Press released a free, open-source two-volume textbook on machine learning systems, covering AI engineering from single-machine to fleet-scale infrastructure. The curriculum includes interactive labs, a custom ML framework, hardware deployment kits, and instructor resources, aiming to train one million AI engineers by 2030.", "body_md": "# Machine Learning Systems\n\nTWO-VOLUME TEXTBOOK\n\n# Machine Learning\n\nSystems.\n\nThe physics of AI engineering.\n\nA rigorous, principles-first treatment of how ML systems are built, optimized, and deployed — from a single machine to fleet-scale infrastructure.\n\nHarvard University · MIT Press 2026\n\nActively maintained\nLast updated April 2026\n[Release notes](https://github.com/harvard-edge/cs249r_book/releases)\n\n## A complete curriculum for AI engineering.\n\nChoose a path: read the books, explore trade-offs in labs, build the internals with TinyTorch, model constraints with MLSys·im, deploy on real hardware, practice with StaffML, or adopt the full course with the Blueprint.\n\nFor Students & Learners\n\n[\n](labs/)\n\nEXPLORE\n\n### Labs\n\nInteractive Marimo notebooks. Change a parameter, see what breaks, build intuition.\n\n[\n](tinytorch/)\n\nBUILD\n\n### TinyTorch\n\nBuild your own ML framework from scratch across 20 progressive modules. Zero magic.\n\n[\n](mlsysim/)\n\nMODEL\n\n### MLSys·im\n\nFirst-principles performance modeling. One command, every bottleneck.\n\n[\n](kits/)\n\nDEPLOY\n\n### Hardware Kits\n\nDeploy ML to Arduino, Seeed, Grove, and Raspberry Pi. Real memory limits, real power budgets.\n\nFor Career & Instructors\n\n[\n](staffml/)\n\nPRACTICE\n\n### StaffML\n\nPhysics-grounded interview questions for ML systems roles. Vault, drills, and mock interviews.\n\n[\n](instructors/)\n\nADOPT\n\n### Instructor Hub\n\nThe AI Engineering Blueprint: two-semester syllabi, pedagogy guide, rubrics, and TA handbook.\n\n[\n](slides/)\n\nTEACH\n\n### Lecture Slides\n\n35 Beamer decks with speaker notes and 266 original SVG diagrams. Drop in and teach.\n\n[\n](newsletter/)\n\nFOLLOW\n\n### Newsletter\n\nUpdates on the curriculum, new chapters, and what the community is building.\n\nOUR MISSION\n\nAI education should be\n\nfree and open to everyone.\n\nEveryone calls AI the new electricity — but electricity is useless without engineers who can build the grid. For AI to be efficient, reliable, and safe, the world needs engineers who understand how to build it.\n\nThat knowledge should be accessible to anyone willing to learn. This curriculum is our commitment to making it so.\n\n23,000+ stars · 243,000+ readers · 180+ countries\n\nOur goal: **1,000,000 AI engineers by 2030**\n\nNext milestone: 100,000 ★ — we're at 23,000+.\n\nEvery star helps others discover this resource.", "url": "https://wpnews.pro/news/machine-learning-systems", "canonical_source": "https://mlsysbook.ai/", "published_at": "2026-06-15 18:14:15+00:00", "updated_at": "2026-06-15 18:38:49.963366+00:00", "lang": "en", "topics": ["machine-learning", "ai-infrastructure", "ai-tools", "developer-tools"], "entities": ["Harvard University", "MIT Press", "TinyTorch", "MLSys·im", "StaffML", "Arduino", "Raspberry Pi"], "alternates": {"html": "https://wpnews.pro/news/machine-learning-systems", "markdown": "https://wpnews.pro/news/machine-learning-systems.md", "text": "https://wpnews.pro/news/machine-learning-systems.txt", "jsonld": "https://wpnews.pro/news/machine-learning-systems.jsonld"}}