Class Featurestore (1.71.0)

Version 1.71.0

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Featurestore(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Attributes
Name	Description
name	str Output only. Name of the Featurestore. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
create_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this Featurestore was created.
update_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this Featurestore was last updated.
etag	str Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	MutableMapping[str, str] Optional. The labels with user-defined metadata to organize your Featurestore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
online_serving_config	google.cloud.aiplatform_v1beta1.types.Featurestore.OnlineServingConfig Optional. Config for online storage resources. The field should not co-exist with the field of OnlineStoreReplicationConfig. If both of it and OnlineStoreReplicationConfig are unset, the feature store will not have an online store and cannot be used for online serving.
state	google.cloud.aiplatform_v1beta1.types.Featurestore.State Output only. State of the featurestore.
online_storage_ttl_days	int Optional. TTL in days for feature values that will be stored in online serving storage. The Feature Store online storage periodically removes obsolete feature values older than online_storage_ttl_days since the feature generation time. Note that online_storage_ttl_days should be less than or equal to offline_storage_ttl_days for each EntityType under a featurestore. If not set, default to 4000 days
encryption_spec	google.cloud.aiplatform_v1beta1.types.EncryptionSpec Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

Classes

LabelsEntry

LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The abstract base class for a message.

Parameters
Name	Description
kwargs	dict Keys and values corresponding to the fields of the message.
mapping	Union[dict, .Message] A dictionary or message to be used to determine the values for this message.
ignore_unknown_fields	Optional(bool) If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.

OnlineServingConfig

OnlineServingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

OnlineServingConfig specifies the details for provisioning online serving resources.

State

State(value)

Possible states a featurestore can have.

Methods

Featurestore

Featurestore(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.