> ## Documentation Index
> Fetch the complete documentation index at: https://zenml.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Feature Stores

> How to manage data in feature stores

Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept in sync between the two. It also offers a centralized registry where features (and feature schemas) are stored for use within a team or wider organization.

As a data scientist working on training your model, your requirements for how you access your batch / 'offline' data will almost certainly be different from how you access that data as part of a real-time or online inference setting. Feast solves the problem of developing [train-serve skew](https://ploomber.io/blog/train-serve-skew/) where those two sources of data diverge from each other.

Feature stores are a relatively recent addition to commonly-used machine learning stacks.

### When to use it

The feature store is an optional stack component in the ZenML Stack. The feature store as a technology should be used to store the features and inject them into the process in the server-side. This includes

* Productionalize new features

* Reuse existing features across multiple pipelines and models

* Achieve consistency between training and serving data (Training Serving Skew)

* Provide a central registry of features and feature schemas

### List of available feature stores

For production use cases, some more flavors can be found in specific `integrations` modules. In terms of features stores, ZenML features an integration of `feast`.

| Feature Store                                                     | Flavor   | Integration | Notes                                                                    |
| ----------------------------------------------------------------- | -------- | ----------- | ------------------------------------------------------------------------ |
| [FeastFeatureStore](/component-gallery/feature-stores/feast)      | feast    | feast       | Connect ZenML with already existing Feast                                |
| [Custom Implementation](/component-gallery/feature-stores/custom) | *custom* |             | Extend the feature store abstraction and provide your own implementation |

If you would like to see the available flavors for feature stores, you can use the command:

```
zenml feature-store flavor list
```

### How to use it

The available implementation of the feature store is built on top of the feast integration, which means that using a feature store is no different from what's described in the [feast page: How to use it?](/component-gallery/feature-stores/feast#how-do-you-use-it).