LLM/VLM training and inference engineering
An open collection of methodologies, tools, and step-by-step commands for training/fine-tuning large language and multimodal models and serving their inference.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
For engineers wanting a broader, product-oriented AI engineering book instead of a training-focused command cookbook.
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
For hands-on LLM/RAG/agent application tutorials rather than large-model training/inference ops.
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
For beginners wanting a single end-to-end small LLM training tutorial, but narrower than the seed's operational breadth.
A straightforward method for training your LLM, from downloading data to generating text.
These projects were analysed and named ml-engineering among their alternatives. The relationship is not symmetric — how ml-engineering rates them is a separate judgement, made when ml-engineering is analysed in its own right.
calls ml-engineering “Choose it when you want an English open book on practical ML engineering and operations rather than the seed's Chinese-first systems curriculum.”
《Machine Learning Systems: Design and Implementation》 (V2 is launching soon）
calls ml-engineering “Open book on ML engineering covering infrastructure and large-scale training; same learning domain, different format (prose book vs. interactive course).”
Learn how to develop, deploy and iterate on production-grade ML applications.
calls ml-engineering “Open book focused on large-scale training and inference engineering; choose it for deep operational details of running huge models instead of broad ML systems coverage.”
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
calls ml-engineering “Choose it when your focus is training and debugging large models rather than building applications with foundation models.”
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
calls ml-engineering “Choose it for a comprehensive open-book reference on ML engineering and training performance instead of a guided course.”
Learn ML engineering for free in 4 months! Register here 👇🏼
calls ml-engineering “Use it for applied training/inference engineering of large models instead of a general theoretical compendium.”
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
calls ml-engineering “choose for deep technical reference on training infrastructure and performance.”
Learn it. Build it. Ship it for others.