stas00/ml-engineering
Machine Learning Engineering Open Book
This open book provides practical instructions, scripts, and methodologies for machine learning engineers to successfully train and fine-tune large language and multi-modal models. It offers guidance on everything from choosing cloud providers and configuring hardware to debugging complex training issues, helping engineers optimize model performance and deployment. The primary users are LLM/VLM training engineers and operators.
17,380 stars. Actively maintained with 1 commit in the last 30 days.
Use this if you are an ML engineer responsible for the hands-on training, fine-tuning, and inference of large-scale AI models, and need concrete solutions and best practices.
Not ideal if you are looking for an introduction to machine learning concepts or a high-level overview of AI, as this material is highly technical and operational.
Stars
17,380
Forks
1,103
Language
Python
License
CC-BY-SA-4.0
Category
Last pushed
Mar 11, 2026
Commits (30d)
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/stas00/ml-engineering"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related models
SwanHubX/SwanLab
⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports...
mdsrqbl/omnihuman
AI model that understands text & humanoids.
labmlai/annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including...
analyticalrohit/AI-ML-Cheatsheets
All Stanford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine...
avikumart/LLM-GenAI-Transformers-Notebooks
An repository containing all the LLM notebooks with tutorial and projects