It was built by a Harvard professor:
Machine Learning Systems by Prof. Vijay Janapa Reddi teaches what really matters when moving ML from a notebook to a production system.
It walks through the entire ML lifecycle:
- Design → scalable and modular ML architectures
- Build → robust data pipelines and feature stores
- Deploy → to cloud, edge, and mobile environments
- Operate → logging, monitoring, rollback (MLOps in action)
- Optimize → for latency, power, and memory at scale
Training the model is only 10% of the job. The real challenge is making it run reliably in production.
Master these 5 phases and you'll know how to design, deploy, and maintain ML systems that are fast, efficient, and production-ready.
That's the skill gap between a $120K and $200K+ ML engineering role.
Inside the repo:
- Full textbook (for free)
- Hands-on labs with real code
- TinyTorch framework for learning
- Instructor resources & teaching guides
Based on Harvard's CS249r course - 100% open-source on GitHub.
Link to the repo 👉 https://lnkd.in/gfpQdM-a
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