> For the complete documentation index, see [llms.txt](https://awsnotes.dendron.so/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://awsnotes.dendron.so/machine-learning/amazon-sagemaker/topics/create-store-and-share-features.md).

# 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.