Class Featurestore (1.48.0)

Version latest

• 1.48.0 (latest)
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

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_v1.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_v1.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_v1.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.

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.

Values: STATE_UNSPECIFIED (0): Default value. This value is unused. STABLE (1): State when the featurestore configuration is not being updated and the fields reflect the current configuration of the featurestore. The featurestore is usable in this state. UPDATING (2): The state of the featurestore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featurestore. For example, online_serving_config.fixed_node_count can take minutes to update. While the update is in progress, the featurestore is in the UPDATING state, and the value of fixed_node_count can be the original value or the updated value, depending on the progress of the operation. Until the update completes, the actual number of nodes can still be the original value of fixed_node_count. The featurestore is still usable in this state.

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.