:relatedlinks: [Diátaxis](https://diataxis.fr/)

.. _home:

Charmed MLflow Documentation
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Charmed MLflow is a platform for managing the end-to-end machine learning lifecycle.

It provides tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models. It integrates with popular machine learning frameworks.

It addresses a number of common machine learning challenges: collaboration, reproducibility, maintenance, organisation and scaling.

It's ideal for data scientists, ML engineers, hobbyists and teams looking to optimise their ML workflows with charms.

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In this documentation
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..  grid:: 1 1 2 2

   ..  grid-item:: :doc:`Tutorial <tutorial/index>`

       **Start here**: a hands-on introduction to Charmed MLflow for newcomers

   ..  grid-item:: :doc:`How-to guides <how-to/index>`

      **Step-by-step guides** covering key operations and common tasks in Charmed MLflow

.. grid:: 1 1 2 2
   :reverse:

   .. grid-item:: :doc:`Reference <reference/index>`

      **Technical information** - specifications, APIs, architecture of Charmed MLflow

   .. grid-item:: :doc:`Explanation <explanation/index>`

      **Discussion and clarification** of key Charmed MLflow concepts and features

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Project and community
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Charmed MLflow is an open-source project that values its community. We warmly welcome contributions, suggestions, fixes, and constructive feedback from everyone.

* `Code of conduct`_
* `Contribute`_
* `Join our online chat`_
* `Upstream Project`_
* `Discourse Forum`_

.. toctree::
   :hidden:
   :maxdepth: 2

   tutorial/index
   how-to/index
   reference/index
   explanation/index
   contributing