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AutoML library for deep learning

ModelScope: bring the notion of Model-as-a-Service to life.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

A Beautiful Open Source RSS & Podcast App Powered by Getstream.io

A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU.

Deep universal probabilistic programming with Python and PyTorch

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

🧙 Build, run, and manage data pipelines for integrating and transforming data.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

My Python scripts to make high-quality figures for publications in top AI conferences and journals.

Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀

Accessible large language models via k-bit quantization for PyTorch.

High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8+ vector databases, and agent orchestration, all from your IDE. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.

A self-hosted open source photo management service.

Production infrastructure for machine learning at scale

tensorboard for pytorch (and chainer, mxnet, numpy, ...)

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

An open source python library for automated feature engineering

Efficiently computes derivatives of NumPy code.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Dynamic, resilient AI orchestration. Coordinate data, models, and compute as you build AI workflows.

The Open Source Feature Store for AI/ML

The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.