MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles three primary functions:
- Tracking experiments to record and compare parameters and results (:ref:`tracking`).
- Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (:ref:`projects`).
- Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (:ref:`models`).
MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a :ref:`rest-api` and :ref:`CLI<cli>`. For convenience, the project also includes a :ref:`python-api`, :ref:`R-api`, and :ref:`java_api`.
.. toctree:: :maxdepth: 1 quickstart tutorial concepts tracking projects models cli python_api/index R-api java_api/index rest-api
The current version of MLflow is a beta release. This means that APIs and storage formats are subject to breaking change.