The AI Engineering Blueprint
A course in a box for teaching AI systems. Two semesters of material, open source and ready to adopt.
| Students need | Python proficiency, matrix multiplication and transposes, and introductory probability. No prior ML coursework. | More → |
| You need | No GPUs and no cluster. Labs run in the browser; hardware kits are optional and the course runs fully without them. | More → |
Syllabi
AI Systems Foundations
From one machine to eight accelerators. Students build TinyTorch from scratch and learn the Iron Law of hardware-software co-design.
Wk 1-4: Physics of AI · Wk 5-8: Building the Stack · Wk 9-12: Optimization · Wk 13-16: Production
Semester 2 · Volume IIAI Engineering at Scale
From one node to ten thousand. Distributed training, collective communication, and fleet infrastructure for frontier models.
Wk 1-4: The Fleet · Wk 5-8: Distributed Algorithms · Wk 9-12: Deployment · Wk 13-16: Governance
Course Materials
| Textbook | Two volumes: Foundations (1–8 GPUs) and At Scale (distributed fleets). HTML, PDF, EPUB. | Vol I · Vol II |
| TinyTorch | Students build a framework from scratch, tensors through transformers, across 20 modules. The tests grade themselves. | Browse → |
| Interactive Labs | Run in the browser on mlsysim with no GPU and nothing to install, so lab access costs nothing to provision. | Browse → |
| Hardware Kits | Arduino Nano 33 BLE, Raspberry Pi with Coral, Seeed XIAO ESP32S3. Optional; the course runs fully without hardware. | Browse → |
| Lecture Slides | One deck per lecture, each with speaker notes and in-class exercises. PDF and PowerPoint, or edit the LaTeX source. | Browse → |
Teaching Resources
| Syllabi | Week-by-week schedules with linked readings, labs, and assignments for both semesters. | Sem 1 · Sem 2 |
| Assessment | Three-tier rubrics, sample student work, AI Olympics capstone spec, grading load estimates. | View → |
| Pedagogy | Prediction Locks, Decision Logs, the A→B→C lab structure, and the Iron Law audit framework. | View → |
| TA Guide | Grading workflows, common student struggles by week, lab facilitation, office hours protocol. | View → |
| Customization | 10-week quarter, 3-day workshop, graduate seminar, embedded/cloud emphases. | View → |
| FAQ | Prerequisites, setup, AI tools policy, hardware budgets, and adoption questions. | View → |
The Books
The two volumes students read. Free online in full, with hardcover editions published by MIT Press.
Companion Books
The MLSysBook curriculum is extended by open companion books authored by members of the TinyML4D Academic Network. These pair directly with the hardware kits used in Semester 1's labs, and are maintained by the original authors.
Ready to Adopt?
Part of the MLSysBook Ecosystem
Textbook
Comprehensive theory across the full ML systems stack.
Lecture Slides
Beamer decks and teaching materials for every chapter.
Labs
Interactive Marimo notebooks that measure the book's claims.
Hardware Kits
Hands-on embedded ML deployment on real devices.
Instructor Hub
Course maps, syllabi, and adoption resources for teaching.
TinyTorch
Build your own ML framework from scratch, module by module.
MLSys·im
The analytical modeling engine behind the book's quantitative figures.