# Create, Store, and Share Features

{% hint style="info" %}
This page was generated from content adapted from the [AWS Developer Guide](https://github.com/awsdocs/amazon-sagemaker-developer-guide.git)
{% endhint %}

## Find Features in Your Feature Groups

* **Important**\
  Use the latest version of Amazon SageMaker Studio to make sure that you're using the most recent version of the search functionality. For information on updating Studio, see [Shut down and Update SageMaker Studio](https://github.com/kevinslin/aws-reference-notes/blob/main/services/amazon_sage_maker/studio-tasks-update-studio.md).

## Find Feature Groups in Your Feature Store

* **Note**\
  The feature groups that you're searching for must be within the same AWS account and AWS Region.
* **Important**\
  Use the latest version of Amazon SageMaker Studio to make sure that you're using the most recent version of the search functionality. For information on updating Studio, see [Shut down and Update SageMaker Studio](https://github.com/kevinslin/aws-reference-notes/blob/main/services/amazon_sage_maker/studio-tasks-update-studio.md).

## Adding Searchable Metadata to Your Features

* **Note**\
  The role that you use must have the following managed policies attached to it: `AmazonS3FullAccess` and `AmazonSageMakerFeatureStoreAccess`.

## Create a Dataset From Your Feature Groups

* **Important**\
  Feature Store requires data to be registered in a AWS Glue data catalog. By default, Feature Store automatically builds an AWS Glue data catalog when you create a feature group.
* **Note**\
  To make sure that your data is up to date, you can set up a AWS Glue crawler to run on a schedule.\
  To set up a AWS Glue crawler, specify an IAM role that the crawler is using to access the offline store’s S3 buckets. For more information, see [Create an IAM role](https://docs.aws.amazon.com/glue/latest/dg/create-an-iam-role.html).\
  For more information on how to use AWS Glue and Athena to build a training dataset for model training and inference, see [Create Feature Groups](https://github.com/kevinslin/aws-reference-notes/blob/main/services/amazon_sage_maker/feature-store-create-feature-group.md).

## Security and Access Control

* **Note**\
  The key policy for the online store also works for the offline store, only when the `kms:ViaService` condition is not specified.
* **Important**\
  You can specify a AWS KMS encryption key to encrypt the Amazon S3 location used for your offline feature store when you create a feature group. If AWS KMS encryption key is not specified, by default we encrypt all data at rest using AWS KMS key. By defining your [bucket-level key](https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucket-key.html) for SSE, you can reduce AWS KMS requests costs by up to 99 percent.

## Quotas, Naming Rules and Data Types

* **Note**\
  Soft limits can be increased based on your needs.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://awsnotes.dendron.so/machine-learning/amazon-sagemaker/topics/create-store-and-share-features.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.