SageMaker Feature Group

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SageMaker Feature Group is a managed resource in Amazon SageMaker that stores and manages machine learning features for training and inference. It provides a centralized, consistent way to define, access, and update features, supporting both real-time and batch use cases. Feature Groups help ensure data consistency between training and production, simplify feature reuse, and integrate with other AWS services for scalable machine learning workflows.

aws.sagemaker_feature_group

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
creation_time	core	timestamp	A timestamp indicating when SageMaker created the FeatureGroup.
description	core	string	A free form description of the feature group.
event_time_feature_name	core	string	The name of the feature that stores the EventTime of a Record in a FeatureGroup. An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup have a corresponding EventTime.
failure_reason	core	string	The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure can occur because: The FeatureGroup could not be created in the OfflineStore. The FeatureGroup could not be deleted from the OfflineStore.
feature_definitions	core	json	A list of the Features in the FeatureGroup. Each feature is defined by a FeatureName and FeatureType.
feature_group_arn	core	string	The Amazon Resource Name (ARN) of the FeatureGroup.
feature_group_name	core	string	he name of the FeatureGroup.
feature_group_status	core	string	The status of the feature group.
last_modified_time	core	timestamp	A timestamp indicating when the feature group was last updated.
last_update_status	core	json	A value indicating whether the update made to the feature group was successful.
offline_store_config	core	json	The configuration of the offline store. It includes the following configurations: Amazon S3 location of the offline store. Configuration of the Glue data catalog. Table format of the offline store. Option to disable the automatic creation of a Glue table for the offline store. Encryption configuration.
offline_store_status	core	json	The status of the OfflineStore. Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked
online_store_config	core	json	The configuration for the OnlineStore.
online_store_total_size_bytes	core	int64	The size of the OnlineStore in bytes.
record_identifier_feature_name	core	string	The name of the Feature used for RecordIdentifier, whose value uniquely identifies a record stored in the feature store.
role_arn	core	string	The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
tags	core	hstore
throughput_config	core	json	Active throughput configuration of the feature group. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled. Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.