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harvard-edge/cs249r_book

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.

Project comparisons

Evidence and sources

README: 📘 Textbook
GitHub project description: Machine Learning Systems
GitHub repository metadata: courseware
README: The world is rushing to build AI systems. It is not engineering them.
README: building efficient, reliable, safe, and robust intelligent systems that operate in the real world, not just models in isolation.

Reviewed at: 2026-07-26T20:17:05Z

Open original GitHub project