> ## Documentation Index > Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # Feast > Integrating Arize with open sourced lightweight feature store, Feast. [Arize and Feast](https://arize.com/blog/feast-and-arize-supercharge-feature-management-and-model-monitoring-for-mlops/) are two platforms aimed at different, but connected, parts of the ML pipeline. Arize helps you visualize your model performance, understand drift & data quality issues, and share insights as your **Evaluation Store**. Feast (i.e, **Feature Store**) is an operational data system for managing and serving machine learning features to models in production. Integration with Feast is simple in four steps. \*\*Step 1: \*\*Log production by calling `arize.log `after materializing and `store.get_online_features`. **Step 2**: At training time, log validation under `batch_id` of `offline`after fetch historical feature `store.get_offline_features`. **Step 3**: Set-up match environment on Arize. S**tep 4:** Troubleshoot and observe any data inconsistency issue with Arize. ## Tutorial Notebook We have set up a very basic example using Feast with Arize.