Harvard's free ML systems curriculum goes full AI engineering, 28K stars on GitHub
techNmak · x · 2026-09-18
Harvard Prof. Vijay Janapa Reddi's Machine Learning Systems project has grown into a complete AI engineering curriculum — focused on what it takes to make an ML model work reliably as a real system.
- Volume I: ML system fundamentals and training through model optimization, hardware acceleration, benchmarking, deployment, serving and operations.
- Volume II: compute infrastructure, distributed training, communication, reliability, inference at scale and fleet operations.
Beyond the two textbooks, it includes 34 browser-based interactive labs, TinyTorch for building the machinery from tensors upward, hardware kits for hands-on deployment, MLSys·im for reasoning about systems, and StaffML for ML systems design practice. The GitHub repo (harvard-edge/cs249rbook) sits at 28K stars.
Many people learn to train a model; far fewer learn to make it work reliably in production — worth checking before paying $2,000 for a course.
More from Companies & People
- Sakana AI expands Tokyo hiring, FIG research collective invites neuroscientists to apply — kaixhin · 2026-09-18
- Anthropic unveils three metrics to track AI self-development, agent oversight and compute allocation — pstAsiatech · 2026-09-18
- Axiom opens 20+ roles across model, eval, hardware design amid hypergrowth — ankurhandos · 2026-09-18
- Musk says X Holdings is taking shape as investor floats Tesla-SpaceX merger — beffjezos · 2026-09-18
- Runway signed major Japanese enterprise deals without a local entity, Japan now its 3rd-largest market — c_valenzuelab · 2026-09-18
- Commenter argues OpenAI and Anthropic are slowing everyone down to keep their Coke-and-Pepsi duopoly — haider1 · 2026-09-18