[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)

🦜🔗 Build context-aware reasoning applications

Documentation synced with GitBook. For all issues with the wandb library, please use https://github.com/wandb/client/issues

A curated list to learn about distributed systems

Source code accompanying O'Reilly book: Machine Learning Design Patterns

The uncompromising Python code formatter

The Language Interpretability Tool: Interactively analyze NLP models for model understanding in an extensible and framework agnostic interface.

Visualizer for neural network, deep learning and machine learning models

Modin: Speed up your Pandas workflows by changing a single line of code

A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning libr…

Uplift modeling package.

The most cited deep learning papers

A library for encrypted, privacy preserving machine learning

MIMIC-Extract:A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III

Accessible AutoML for deep learning.

Automated Machine Learning with scikit-learn

DGL tutorial in KDD 2019

Best Practices on Recommendation Systems

A tutorial demonstrating how to implement deep learning models for time series forecasting

Labs and demos for courses for GCP Training (http://cloud.google.com/training).

A list of useful resources to learn Data Engineering from scratch