TWO-VOLUME TEXTBOOK
Systems.
The physics of AI engineering.
A rigorous, principles-first treatment of how ML systems are built, optimized, and deployed — from a single machine to fleet-scale infrastructure.
Harvard University · MIT Press 2026
Actively maintained Last updated April 2026
A complete curriculum for AI engineering. #
Choose 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.
For Students & Learners
[
](labs/)
EXPLORE
Labs
Interactive Marimo notebooks. Change a parameter, see what breaks, build intuition.
[
](tinytorch/)
BUILD
TinyTorch
Build your own ML framework from scratch across 20 progressive modules. Zero magic.
[
](mlsysim/)
MODEL
MLSys·im
First-principles performance modeling. One command, every bottleneck.
[
](kits/)
DEPLOY
Hardware Kits
Deploy ML to Arduino, Seeed, Grove, and Raspberry Pi. Real memory limits, real power budgets.
For Career & Instructors
[
](staffml/)
PRACTICE
StaffML
Physics-grounded interview questions for ML systems roles. Vault, drills, and mock interviews.
[
](instructors/)
ADOPT
Instructor Hub
The AI Engineering Blueprint: two-semester syllabi, pedagogy guide, rubrics, and TA handbook.
[
](slides/)
TEACH
Lecture Slides
35 Beamer decks with speaker notes and 266 original SVG diagrams. Drop in and teach.
[
](newsletter/)
FOLLOW
Newsletter
Updates on the curriculum, new chapters, and what the community is building.
OUR MISSION
AI education should be
free and open to everyone.
Everyone 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.
That knowledge should be accessible to anyone willing to learn. This curriculum is our commitment to making it so.
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