@cevenif: 90% of machine learning tutorials on the market are actually misleading you—what's the point of just training a model? If it can't go into production, all the earlier effort is wasted. Seriously, I've seen too many people fall into this trap: they follow tutorials and train models like crazy, but when they put them into real-world environments, they immediately break—they don't know how to deploy, can't set up monitoring, and scalability is a mess. Harvard University directly...
Summary
Harvard University open-sourced the textbook "Machine Learning Systems," which systematically covers practical topics such as ML system design, data engineering, model deployment, MLOps, and edge AI, aiming to help bring AI from research into production. It is freely available on GitHub.
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Cached at: 06/17/26, 01:44 AM
English • Chinese • Japanese • Korean
📘 Textbook • 📗 Vol I + 📘 Vol II • 🔥 TinyTorch • 🔬 Labs • 🔮 MLSys·im • 💼 StaffML
📚 Hardcopy edition coming 2026 with MIT Press.
The world is rushing to build AI systems. It is not engineering them.
The repository is the curriculum.
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The two-volume split replaces the single-volume edition at launch.
Coming soon!
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