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

.. _home:

Charmed MLflow documentation
============================

Charmed MLflow is a platform for managing the end-to-end Machine Learning (ML) lifecycle.

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

It also addresses a number of common ML challenges including collaboration, reproducibility, maintenance, organisation and scaling.

It is intended for data scientists and ML engineers, looking to optimise their ML workflows with charms.

---------

In this documentation
---------------------

..  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

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

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

---------

Project and community
---------------------

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

* Read our `Code of conduct`_.
* `Contribute`_ and `report bugs <https://github.com/canonical/mlflow-operator/issues/new/choose>`_.
* Join the `Discourse forum`_.
* `Talk to us on Matrix <https://matrix.to/#/#charmhub-mlops-kubeflow:ubuntu.com>`_.
* Learn more about the `upstream project`_.

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

   Home <self>
   tutorial/index
   How to <how-to/index>
   explanation/index
   Contribute <contributing>