MLflow
Open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. Now also covers LLM/agent observability and evaluation.
Visit siteOpen Source MLOps Platforms & Tools
A hand-verified directory of open source platforms and tools for managing the machine learning lifecycle — experiment tracking, pipeline orchestration, model deployment, and continuous training.
14Tools listed
Kubeflow
The foundation of tools for AI platforms on Kubernetes, making deployment of ML workflows simple, portable, and scalable.
Visit siteMLRun
Open source AI orchestration framework to manage ML and generative AI applications from research to production-ready applications.
Visit siteZenML
A simple yet powerful open-source framework that scales your MLOps stack with your needs, from classical ML pipelines to LLM and agent workflows.
Visit siteFlyte
The workflow automation platform for complex, mission-critical data and machine learning processes at scale, authored in pure Python.
Visit siteSeldon
MLOps platform to deploy, monitor, and explain machine learning models in production, with built-in drift detection and explainability (XAI).
Visit siteKedro
Open-source Python framework for creating reproducible, maintainable, and modular data engineering and data science code.
Visit siteMetaflow
A framework for real-life ML, AI, and data science that makes it quick and easy to build and manage production-grade projects. Originally built at Netflix.
Visit sitePachyderm
Automates and scales the machine learning lifecycle while guaranteeing reproducibility, combining data lineage with end-to-end pipelines on Kubernetes.
Visit sitePyMLPipe
A lightweight MLOps Python package for model monitoring, deployment, and explainable AI, supporting scikit-learn, XGBoost, LightGBM, and PyTorch.
Visit siteCML
Continuous Machine Learning brings your favorite DevOps tools — GitHub, GitLab, Bitbucket CI/CD — to machine learning projects.
Visit siteCortex
Cloud infrastructure to deploy, manage, and scale machine learning models in production on AWS. Cortex Labs joined Databricks in 2022; the open-source project site remains live and on-topic.
Visit siteAutoKeras
An AutoML system based on Keras, developed by DATA Lab at Texas A&M University, with the goal of making machine learning accessible to everyone.
Visit siteH2O Open Source AutoML
Train the best model in the least amount of time using a simple interface in R, Python, or a web GUI — part of the open source H2O-3 platform.
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