An integrated, hands-on curriculum teaching the principles and practices of engineering end-to-end intelligent systems. Includes textbooks, interactive labs, hardware deployment kits, an infrastructure simulator, and interview practice tools.
Project overview
Addresses the gap between training machine learning models and engineering reliable real-world AI systems by teaching underlying physics and quantitative reasoning rather than tool usage alone.
Project type
Model Runtime · Infrastructure · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
License pending
Best for
Educators seeking course adoption materials, including syllabi, pedagogy guides, assessment rubrics, and TA handbooks.
Learners and developers building an understanding of ML systems from the ground up by building an ML framework from scratch.
Key capabilities
A two-volume textbook teaching the theory, mental models, and quantitative reasoning behind ML systems, following the Hennessy & Patterson pedagogical model.
A progressive educational module set to build your own ML framework from scratch across 20 modules.
A simulator to calculate memory bottlenecks, network saturation, and scheduling limits at infrastructure scales you can't physically access.
Physics-grounded interview questions for ML systems roles, including a vault, practice drills, mock interviews, and progress tracking.
Hardware kits for deploying ML to Arduino, Seeed, Grove, and Raspberry Pi devices to face real memory limits, power budgets, and latency.
Provides the AI Engineering Blueprint: two 16-week syllabi, pedagogy guide, assessment rubrics, and a TA handbook for educators.
Beamer slide decks for every chapter, with four theme variants, ready for course adoption.
An experimental project exploring AI-guided reading, contextual quizzes, and spaced repetition for static learning sites.
Limitations and risks
This is not an MLOps guide or operations manual for wiring today's tools.
Getting started
The primary interaction is via a hosted web textbook requiring no setup. Users can begin by reading the textbook at mlsysbook.ai.
Alternatives and comparisons
A free course teaching MLOps fundamentals, covering experiment tracking, orchestration, deployment, and monitoring.
A course on ML engineering covering regression, classification, deep learning, and production deployment.