Chip Huyen

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

Companion materials for the book "AI Engineering" covering how to build real-world applications on top of large language models, including RAG, fine-tuning, evaluation, hallucination reduction, and production optimization.

Summaries and resources for Designing Machine Learning Systems book (Chip Huyen, O'Reilly 2022)

Claude Code dashboard with usage stats, error analysis, and sharable feature

https://huyenchip.com/ml-interviews-book/

Library to scrape and clean web pages to create massive datasets.

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`

A free booklet on designing machine learning systems end-to-end, from project setup through data pipelines, model training, and production deployment, plus 27 open ML interview questions with community answers.

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

Code examples from Stanford's CS 20 TensorFlow for Deep Learning Research course, covering deep learning, NLP, and chatbot construction in Python and TensorFlow 1.4.

Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

Returns latest research results by crawling arxiv papers and summarizing abstracts. Helps you stay afloat with so many new papers everyday.

An ongoing list of pandas quirks

My implementation of useful data structures, algorithms, as well as my solutions to programming puzzles.

Code for O'Reilly's "A Short Course on TensorFlow"

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Metaflow tutorials for ODSC West 2021

Code Repository for Machine Learning with PyTorch and Scikit-Learn

A living collection of deep learning problems

Easy TOC creation for GitHub README.md

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

Computation using data flow graphs for scalable machine learning

Toolkit for efficient experimentation with various sequence-to-sequence models

The flexibility of Python with the scale and performance of modern SQL.

A library for generalized sequence to sequence models

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Library for fast text representation and classification.

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Feathr – An Enterprise-Grade, High Performance Feature Store

LSTM and QRNN Language Model Toolkit for PyTorch

Open deep learning compiler stack for cpu, gpu and specialized accelerators

MathJAX plugin for GitBook