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The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
.NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers.
Elyra extends JupyterLab with an AI centric approach.
One advanced and mature open-source MPP (Massively Parallel Processing) database. Open source alternative to Greenplum Database.
PySpark + Scikit-learn = Sparkit-learn
R interface for Apache Spark
A scalable machine learning library on Apache Spark
[DEPRECATED] Tensorflow wrapper for DataFrames on Apache Spark
RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
Godot-MCP — Model Context Protocol (MCP) integration for the Godot Engine. AI tools for the Godot Editor in C#, with cloud connection to ai-game.dev. Apache-2.0.
Kubernetes operator for self-hosted LLM inference across a heterogeneous GPU fleet: NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. Runtimes: llama.cpp, vLLM, TGI, mlx-server. Multi-GPU sharding, model caching, OpenAI-compatible endpoints. Apache-2.0, run across homelab and on-prem fleets, actively developed.
Apache cTAKES is a Natural Language Processing (NLP) platform for clinical text.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Machine learning and natural language processing with Apache Pig
Pin AI Skill behavior. Block unapproved drift in CI. See exactly what changed in every PR.