SINGLE-MACHINE SYSTEMS
Introduction to Machine Learning Systems
Data pipelines, training, optimization, and deployment, with a semester syllabus and implementation work.
LEARN · BUILD · TEACH
Learn and teach with MLSysBook.
Explore readings, experiments, and teaching plans for machine learning systems. Follow a learning path or adapt the materials for a course.
Teaching your own course? Visit the Instructor HubCourse models and teaching materials
These materials show how to organize MLSysBook readings and experiments into a course. Use a model as a starting point, adapt an existing offering, or contribute materials from your own teaching.
DISTRIBUTED SYSTEMS
Scaling Machine Learning Systems
Distributed training, communication, fault recovery, and serving, with systems labs and a capstone.
EMBEDDED MACHINE LEARNING
TinyML
Model training and deployment on embedded devices, with syllabi, assignments, and example projects.
INFERENCE NEAR THE DATA
Edge AI
Deployment on Linux edge devices, with hardware guides and companion teaching materials.
ROBOTICS & LEARNED CONTROL
Physical AI
Robot policies, physical limits, and release decisions supported by measurements.
Community courses and workshops
Instructor-maintained offeringsAlso teaching with it
Instructors named in the Vol. I acknowledgments as early adopters, whose course pages are not public.
Teaching with the book?
Share your syllabus or course website with the community.
Thinking of teaching it?
The Instructor Hub has syllabi, slides, labs, and a course map.
Listed courses were added by their instructors, or have a public page, syllabus, or repository that assigns or links the book. Most courses never publish a reading list, so this is a partial list. If yours is listed and you would rather it were not, open an issue.