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MLflow Documentation

MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles four 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`).
  • Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (:ref:`registry`).

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`.

Get started using the :ref:`quickstart` or by reading about the :ref:`key concepts<concepts>`.

.. toctree::
    :maxdepth: 1

    quickstart
    tutorials-and-examples/index
    concepts
    tracking
    projects
    models
    model-registry
    plugins
    cli
    search-syntax
    python_api/index
    R-api
    java_api/index
    rest-api