<commit_before>.. _pipelines:

===============================
MLflow Pipelines (experimental)
===============================

MLflow Pipelines is an opinionated framework for structuring MLOps workflows that simplifies and
standardizes machine learning application development and productionization. MLflow Pipelines
makes it easy for data scientists to follow best practices for creating production-ready ML
deliverables, allowing them to focus on developing excellent models. MLflow Pipelines also enables
ML engineers and DevOps teams to seamlessly deploy these models to production and incorporate them
into applications.

MLflow Pipelines provides production-quality :ref:`Pipeline Templates <pipeline-templates>` for
common ML problem types, such as regression & classification, and MLOps tasks, such as batch
scoring. Pipelines are structured as git repositories with YAML-based configuration files and
Python code, offering developers a declarative approach to ML application development that reduces
boilerplate.

MLflow Pipelines also implements a cache-aware executor for :ref:`Pipeline Steps
<steps-key-concept>`, ensuring that steps are only executed when associated :py:ref:`code or
configurations <pipeline-repositories-key-concept>` have changed. This enables data scientists,
ML engineers, and DevOps teams to iterate very quickly within their domains of expertise. MLflow
offers :ref:`APIs <pipelines-apis>` and a :ref:`CLI <cli>` for executing pipelines and inspecting
their results.

Installation
------------
MLflow Pipelines is available as an extension of the
`MLflow Python library <https://pypi.org/project/mlflow/>`_. You can install MLflow Pipelines
from PyPI as follows:

.. code-block:: sh

    pip install mlflow[pipelines]  # for pip

.. note::
  MLflow Pipelines requires Make, which may not be preinstalled on some systems (e.g. Windows).
  Please ensure Make is installed before using MLflow Pipelines.

.. note::
  You can install MLflow Pipelines from a Databricks Notebook by running
  ``%pip install mlflow[pipelines]`` or install MLflow Pipelines on a Databricks Cluster
  by following the instructions at
  https://docs.databricks.com/libraries/cluster-libraries.html#install-a-library-on-a-cluster.

  Note that `Databricks Runtime <https://docs.databricks.com/runtime/dbr.html>`_ version 11.0
  or greater is required in order to install MLflow Pipelines on Databricks.
<commit_msg>Clarify MLflow Pipelines classification capability (#6982)<commit_after>.. _pipelines:

===============================
MLflow Pipelines (experimental)
===============================

MLflow Pipelines is an opinionated framework for structuring MLOps workflows that simplifies and
standardizes machine learning application development and productionization. MLflow Pipelines
makes it easy for data scientists to follow best practices for creating production-ready ML
deliverables, allowing them to focus on developing excellent models. MLflow Pipelines also enables
ML engineers and DevOps teams to seamlessly deploy these models to production and incorporate them
into applications.

MLflow Pipelines provides production-quality :ref:`Pipeline Templates <pipeline-templates>` for
common ML problem types, such as regression & classification (not yet available, upcoming),
and MLOps tasks, such as batch scoring. Pipelines are structured as git repositories with
YAML-based configuration files and Python code, offering developers a declarative approach
to ML application development that reduces boilerplate.

MLflow Pipelines also implements a cache-aware executor for :ref:`Pipeline Steps
<steps-key-concept>`, ensuring that steps are only executed when associated :py:ref:`code or
configurations <pipeline-repositories-key-concept>` have changed. This enables data scientists,
ML engineers, and DevOps teams to iterate very quickly within their domains of expertise. MLflow
offers :ref:`APIs <pipelines-apis>` and a :ref:`CLI <cli>` for executing pipelines and inspecting
their results.

Installation
------------
MLflow Pipelines is available as an extension of the
`MLflow Python library <https://pypi.org/project/mlflow/>`_. You can install MLflow Pipelines
from PyPI as follows:

.. code-block:: sh

    pip install mlflow[pipelines]  # for pip

.. note::
  MLflow Pipelines requires Make, which may not be preinstalled on some systems (e.g. Windows).
  Please ensure Make is installed before using MLflow Pipelines.

.. note::
  You can install MLflow Pipelines from a Databricks Notebook by running
  ``%pip install mlflow[pipelines]`` or install MLflow Pipelines on a Databricks Cluster
  by following the instructions at
  https://docs.databricks.com/libraries/cluster-libraries.html#install-a-library-on-a-cluster.

  Note that `Databricks Runtime <https://docs.databricks.com/runtime/dbr.html>`_ version 11.0
  or greater is required in order to install MLflow Pipelines on Databricks.

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