Create, Store, and Share Features

This page was generated from content adapted from the AWS Developer Guide

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.

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.

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. 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.

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 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.

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Last updated 1 year ago

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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.

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. 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.

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 for SSE, you can reduce AWS KMS requests costs by up to 99 percent.