Vertex AI Feature Store

Vertex AI Feature Store is a managed service on Google Cloud that helps you store, manage, and serve machine learning features at scale. It provides a centralized repository for feature data, ensuring consistency between training and serving environments. The service supports real-time and batch feature serving, enabling low-latency predictions and efficient reuse of features across models. It also integrates with other Vertex AI components, simplifying the end-to-end ML workflow.

gcp.aiplatform_featurestore

Fields

TitleIDTypeData TypeDescription
_keycorestring
ancestorscorearray<string>
create_timecoretimestampOutput only. Timestamp when this Featurestore was created.
datadog_display_namecorestring
encryption_speccorejsonOptional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key.
etagcorestringOptional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labelscorearray<string>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.
namecorestringOutput only. Name of the Featurestore. Format: `projects/{project}/locations/{location}/featurestores/{featurestore}`
online_serving_configcorejsonOptional. 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.
online_storage_ttl_dayscoreint64Optional. 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
organization_idcorestring
parentcorestring
project_idcorestring
project_numbercorestring
resource_namecorestring
satisfies_pzicoreboolOutput only. Reserved for future use.
satisfies_pzscoreboolOutput only. Reserved for future use.
statecorestringOutput only. State of the featurestore.
tagscorehstore
update_timecoretimestampOutput only. Timestamp when this Featurestore was last updated.