Open Source MLOps Tools & Platforms
- 01. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
- 02. The Kubeflow project is to make deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable.
- 03. MLRun is an open source framework to orchestrate MLOps from the research stage to production-ready AI applications.
- 04. ZenML simplifies and standardizes your MLOps processes. Structure code in pipelines. Integrate your tools. Deploy to the cloud.
- 05. Flyte - The Workflow Automation Platform for Complex, Mission-Critical Data and Machine Learning Processes at Scale
- 06. Seldon MLOps Platform - Deploy, monitor and explain machine learning models.
- 07. Kedro is an open-sourced Python framework for creating maintainable and modular data science code.
- 08. Metaflow makes it quick and easy to build and manage real-life data science projects.
- 09. Pachyderm automates and scales the machine learning lifecycle while guaranteeing reproducibility
- 10. PyMLPipe: A lightweight MLOps Python Package
- 11. MLReef unifies a complete ML pipeline in one solution to address all issues like managing teams and members, project creation, versioning of data and models and much more
- 12. CML helps you bring your favorite DevOps tools to machine learning.
- 13. Cortex - Deploy, manage, and scale machine learning models in production.
- 14. AutoKeras: An AutoML system based on Keras. The goal of AutoKeras is to make machine learning accessible to everyone.
- 15. H2O Open Source AutoML = Train the best model in the least amount of time to save human hours, using a simple interface in R, Python, or a web GUI.