:orphan:

Feast Integration
-----------------

`Feast <https://feast.dev/>`__ is an operational data system for managing and serving machine learning features to models in production.

Flyte provides a way to train models and perform feature engineering as a single pipeline.
But it provides no way to serve these features to production when the model matures and is ready to be served in production.

This is where Feast can be helpful.

On leveraging the collective capabilities, Flyte enables engineering the features, and Feast provides the feature registry and online feature serving system.
Moreover, Flyte can help ensure incremental development of features and enables to turn on the sync to online stores only when one is confident about the features.

In this tutorial, we'll walk through how Feast can be used to store and retrieve features to train and test the model through a pipeline curated using Flyte.

Dataset
=======
We'll use the horse colic dataset to determine if the lesion of the horse is surgical or not. This is a modified version of the original dataset.

The dataset will have the following columns:

.. list-table:: Horse Colic Features
    :widths: 25 25 25 25 25

    * - surgery
      - Age
      - Hospital Number
      - rectal temperature
      - pulse
    * - respiratory rate
      - temperature of extremities
      - peripheral pulse
      - mucous membranes
      - capillary refill time
    * - pain
      - peristalsis
      - abdominal distension
      - nasogastric tube
      - nasogastric reflux
    * - nasogastric reflux PH
      - rectal examination
      - abdomen
      - packed cell volume
      - total protein
    * - abdominocentesis appearance
      - abdomcentesis total protein
      - outcome
      - surgical lesion
      - timestamp

The horse colic dataset will be a compressed zip file consisting of the SQLite DB.
For this example, we wanted a dataset available online, but this could be easily plugged into another dataset/data management system
like Snowflake, Athena, Hive, BigQuery, or Spark, all of which are supported by Flyte.

Takeaways
=========

#. Source data is from SQL-like data sources
#. Procreated feature transforms
#. Serve features to production using Feast

.. panels::
    :header: text-center
    :column: col-lg-12 p-2

    .. link-button:: https://blog.flyte.org/bring-ml-close-to-data-using-feast-and-flyte
       :type: url
       :text: Blog Post
       :classes: btn-block stretched-link
    ^^^^^^^^^^^^
    An article detailing Feast and Flyte integration.

.. toctree::
    :maxdepth: -1
    :caption: Contents
    :hidden:

    Blog Post <https://blog.flyte.org/bring-ml-close-to-data-using-feast-and-flyte>



.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Let&#x27;s define some feature engineering tasks to be used in conjunction with the Flyte workflow.">

.. only:: html

  .. image:: /auto/case_studies/feature_engineering/feast_integration/images/thumb/sphx_glr_feature_eng_tasks_thumb.png
    :alt: Feature Engineering Tasks

  :ref:`sphx_glr_auto_case_studies_feature_engineering_feast_integration_feature_eng_tasks.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Feature Engineering Tasks</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="In this example, let&#x27;s build a Flyte pipeline with Feast as the feature store.">

.. only:: html

  .. image:: /auto/case_studies/feature_engineering/feast_integration/images/thumb/sphx_glr_feast_workflow_thumb.png
    :alt: Flyte Pipeline with Feast

  :ref:`sphx_glr_auto_case_studies_feature_engineering_feast_integration_feast_workflow.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Flyte Pipeline with Feast</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="The goal of this notebook is to train a simple `Gaussian Naive Bayes model using sklearn &lt;https...">

.. only:: html

  .. image:: /auto/case_studies/feature_engineering/feast_integration/images/thumb/sphx_glr_Feast_Flyte_Demo_thumb.png
    :alt: How to Trigger the Feast Workflow using FlyteRemote

  :ref:`sphx_glr_auto_case_studies_feature_engineering_feast_integration_Feast_Flyte_Demo.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">How to Trigger the Feast Workflow using FlyteRemote</div>
    </div>


.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto/case_studies/feature_engineering/feast_integration/feature_eng_tasks
   /auto/case_studies/feature_engineering/feast_integration/feast_workflow
   /auto/case_studies/feature_engineering/feast_integration/Feast_Flyte_Demo


.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-gallery

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download all examples in Python source code: feast_integration_python.zip </auto/case_studies/feature_engineering/feast_integration/feast_integration_python.zip>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download all examples in Jupyter notebooks: feast_integration_jupyter.zip </auto/case_studies/feature_engineering/feast_integration/feast_integration_jupyter.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_