The Production ML Playbook Most Curricula Skip Is Now Free on GitHub

Harvard’s Senior AI Engineer path is now open to everyone for free.
And Prof. Vijay Janapa Reddi put the full ML Systems (CS249r) class on GitHub.
That’s a real shortcut if you want to learn what top teams do in the real world.
The big pieces are all there:
1. Architecture
2. Data pipelines
3. Production
4. MLOps
5. Edge AI
6. Privacy
This is the part of Big Tech that usually stays behind closed doors.
Now it’s out in the open.
Study it.
Save it.
Use it.
Link: https://lnkd.in/d7-3TvEm

Core facts are accurate, but "Senior AI Engineer path" is a mischaracterization worth correcting.

• "Prof. Vijay Janapa Reddi put the full ML Systems (CS249r) class on GitHub" — accurate. The course, taught at Harvard as CS249r, has been developed into an open-source textbook by Reddi and his students/TAs, publicly hosted on GitHub.
• "That's a real shortcut if you want to learn what top teams do in the real world" — reasonable characterization. The course explicitly focuses on the operational side of ML that most curricula skip: deploying, scaling, and maintaining models in production rather than just training them.
• The six pillars: Architecture, Data pipelines, Production, MLOps, Edge AI, Privacy — accurate. These match the course's actual organizing structure exactly.
• "This is the part of Big Tech that usually stays behind closed doors. Now it's out in the open" — reasonable framing; the material does cover topics (production reliability, edge deployment, privacy-preserving compute) that are typically learned on the job rather than in standard ML coursework.
• One correction needed: "Harvard's Senior AI Engineer path" — this framing is misleading. CS249r is not a formal "Senior AI Engineer" credential or career path; it's a graduate-level systems course turned into a free, open-source textbook. There's no official Harvard program by that name being referenced here — it's a single course's material, not a structured career track. Worth softening this claim so it doesn't overstate what's actually being offered.
• Worth knowing before you post this as a shortcut: some critics who've reviewed the open-sourced material caution that it teaches the vocabulary and mental models of production ML systems well, but isn't a substitute for the hands-on experience of actually working inside those systems — worth keeping the framing to "a genuinely rigorous reference," not a full replacement for real-world practice.