The AI Engineering Blueprint
Everything an instructor needs to teach AI Systems — two semesters of syllabi, interactive labs, a build-from-scratch framework, hardware kits, and a complete assessment system. Open-source and ready to adopt.
2Volumes
33Chapters
20Modules
32Labs
35Slide Decks
3HW Kits
600+Tests
ReadTextbook
BuildTinyTorch
ExploreLabs
DeployHW Kits
Course Materials
| Textbook | Two volumes: Foundations (1–8 GPUs) and At Scale (distributed fleets). HTML, PDF, EPUB. | Vol I · Vol II |
| TinyTorch | 20-module framework students build from scratch — tensors to transformers. 600+ auto-graded tests. | Browse → |
| Interactive Labs | 32 Marimo notebooks powered by mlsysim. Browser-based, zero GPU required. | Browse → |
| Hardware Kits | Arduino Nano 33 BLE, Raspberry Pi + Coral, Seeed XIAO ESP32S3. Optional but powerful. | Browse → |
| Lecture Slides | 35 Beamer decks with speaker notes, active learning, and 266 SVG diagrams. PDF and PowerPoint. | 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 → |
Syllabi
Semester 1 · Volume I
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