Class FeaturestoreServiceClient (1.11.0) 

FeaturestoreServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1.services.featurestore_service.transports.base.FeaturestoreServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

The service that handles CRUD and List for resources for Featurestore.

Inheritance

builtins.object > FeaturestoreServiceClient

Properties

transport

Returns the transport used by the client instance.

Returns
Type Description
FeaturestoreServiceTransport The transport used by the client instance.

Methods

FeaturestoreServiceClient

FeaturestoreServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1.services.featurestore_service.transports.base.FeaturestoreServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiates the featurestore service client.

Parameters
Name Description
credentials Optional[google.auth.credentials.Credentials]

The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.
transport Union[str, FeaturestoreServiceTransport]

The transport to use. If set to None, a transport is chosen automatically.
client_options google.api_core.client_options.ClientOptions

Custom options for the client. It won't take effect if a transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.
client_info google.api_core.gapic_v1.client_info.ClientInfo

The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you're developing your own client library.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If mutual TLS transport creation failed for any reason.

__exit__

__exit__(type, value, traceback)

Releases underlying transport's resources.

batch_create_features

batch_create_features(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.BatchCreateFeaturesRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[Sequence[google.cloud.aiplatform_v1.types.featurestore_service.CreateFeatureRequest]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a batch of Features in a given EntityType.

from google.cloud import aiplatform_v1

def sample_batch_create_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    requests = aiplatform_v1.CreateFeatureRequest()
    requests.parent = "parent_value"
    requests.feature.value_type = "BYTES"
    requests.feature_id = "feature_id_value"

    request = aiplatform_v1.BatchCreateFeaturesRequest(
        parent="parent_value",
        requests=requests,
    )

    # Make the request
    operation = client.batch_create_features(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.BatchCreateFeaturesRequest, dict]

The request object. Request message for FeaturestoreService.BatchCreateFeatures.
parent str

Required. The resource name of the EntityType to create the batch of Features under. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
requests Sequence[google.cloud.aiplatform_v1.types.CreateFeatureRequest]

Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType. The parent field in each child request message can be omitted. If parent is set in a child request, then the value must match the parent value in this request message. This corresponds to the requests field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be BatchCreateFeaturesResponse Response message for FeaturestoreService.BatchCreateFeatures.

batch_read_feature_values

batch_read_feature_values(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.BatchReadFeatureValuesRequest, dict]] = None, *, featurestore: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Batch reads Feature values from a Featurestore. This API enables batch reading Feature values, where each read instance in the batch may read Feature values of entities from one or more EntityTypes. Point-in-time correctness is guaranteed for Feature values of each read instance as of each instance's read timestamp.

from google.cloud import aiplatform_v1

def sample_batch_read_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    csv_read_instances = aiplatform_v1.CsvSource()
    csv_read_instances.gcs_source.uris = ['uris_value_1', 'uris_value_2']

    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    entity_type_specs = aiplatform_v1.EntityTypeSpec()
    entity_type_specs.entity_type_id = "entity_type_id_value"
    entity_type_specs.feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']

    request = aiplatform_v1.BatchReadFeatureValuesRequest(
        csv_read_instances=csv_read_instances,
        featurestore="featurestore_value",
        destination=destination,
        entity_type_specs=entity_type_specs,
    )

    # Make the request
    operation = client.batch_read_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.BatchReadFeatureValuesRequest, dict]

The request object. Request message for FeaturestoreService.BatchReadFeatureValues.
featurestore str

Required. The resource name of the Featurestore from which to query Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore} This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be BatchReadFeatureValuesResponse Response message for FeaturestoreService.BatchReadFeatureValues.

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

create_entity_type

create_entity_type(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.CreateEntityTypeRequest, dict]] = None, *, parent: Optional[str] = None, entity_type: Optional[google.cloud.aiplatform_v1.types.entity_type.EntityType] = None, entity_type_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a new EntityType in a given Featurestore.

from google.cloud import aiplatform_v1

def sample_create_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateEntityTypeRequest(
        parent="parent_value",
        entity_type_id="entity_type_id_value",
    )

    # Make the request
    operation = client.create_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateEntityTypeRequest, dict]

The request object. Request message for FeaturestoreService.CreateEntityType.
parent str

Required. The resource name of the Featurestore to create EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
entity_type google.cloud.aiplatform_v1.types.EntityType

The EntityType to create. This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
entity_type_id str

Required. The ID to use for the EntityType, which will become the final component of the EntityType's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a featurestore. This corresponds to the entity_type_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be EntityType An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

create_feature

create_feature(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.CreateFeatureRequest, dict]] = None, *, parent: Optional[str] = None, feature: Optional[google.cloud.aiplatform_v1.types.feature.Feature] = None, feature_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a new Feature in a given EntityType.

from google.cloud import aiplatform_v1

def sample_create_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    feature = aiplatform_v1.Feature()
    feature.value_type = "BYTES"

    request = aiplatform_v1.CreateFeatureRequest(
        parent="parent_value",
        feature=feature,
        feature_id="feature_id_value",
    )

    # Make the request
    operation = client.create_feature(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateFeatureRequest, dict]

The request object. Request message for FeaturestoreService.CreateFeature.
parent str

Required. The resource name of the EntityType to create a Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
feature google.cloud.aiplatform_v1.types.Feature

Required. The Feature to create. This corresponds to the feature field on the request instance; if request is provided, this should not be set.
feature_id str

Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType. This corresponds to the feature_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be Feature Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple.

create_featurestore

create_featurestore(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.CreateFeaturestoreRequest, dict]] = None, *, parent: Optional[str] = None, featurestore: Optional[google.cloud.aiplatform_v1.types.featurestore.Featurestore] = None, featurestore_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a new Featurestore in a given project and location.

from google.cloud import aiplatform_v1

def sample_create_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateFeaturestoreRequest(
        parent="parent_value",
        featurestore_id="featurestore_id_value",
    )

    # Make the request
    operation = client.create_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateFeaturestoreRequest, dict]

The request object. Request message for FeaturestoreService.CreateFeaturestore.
parent str

Required. The resource name of the Location to create Featurestores. Format: projects/{project}/locations/{location}' This corresponds to the parent field on the request instance; if request is provided, this should not be set.
featurestore google.cloud.aiplatform_v1.types.Featurestore

Required. The Featurestore to create. This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
featurestore_id str

Required. The ID to use for this Featurestore, which will become the final component of the Featurestore's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within the project and location. This corresponds to the featurestore_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be Featurestore 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.

delete_entity_type

delete_entity_type(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.DeleteEntityTypeRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a single EntityType. The EntityType must not have any Features or force must be set to true for the request to succeed.

from google.cloud import aiplatform_v1

def sample_delete_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteEntityTypeRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteEntityTypeRequest, dict]

The request object. Request message for [FeaturestoreService.DeleteEntityTypes][].
name str

Required. The name of the EntityType to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the name field on the request instance; if request is provided, this should not be set.
force bool

If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.) This corresponds to the force field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_feature

delete_feature(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.DeleteFeatureRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a single Feature.

from google.cloud import aiplatform_v1

def sample_delete_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeatureRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_feature(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteFeatureRequest, dict]

The request object. Request message for FeaturestoreService.DeleteFeature.
name str

Required. The name of the Features to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_featurestore

delete_featurestore(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.DeleteFeaturestoreRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a single Featurestore. The Featurestore must not contain any EntityTypes or force must be set to true for the request to succeed.

from google.cloud import aiplatform_v1

def sample_delete_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeaturestoreRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteFeaturestoreRequest, dict]

The request object. Request message for FeaturestoreService.DeleteFeaturestore.
name str

Required. The name of the Featurestore to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore} This corresponds to the name field on the request instance; if request is provided, this should not be set.
force bool

If set to true, any EntityTypes and Features for this Featurestore will also be deleted. (Otherwise, the request will only work if the Featurestore has no EntityTypes.) This corresponds to the force field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

entity_type_path

entity_type_path(project: str, location: str, featurestore: str, entity_type: str)

Returns a fully-qualified entity_type string.

export_feature_values

export_feature_values(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.ExportFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Exports Feature values from all the entities of a target EntityType.

from google.cloud import aiplatform_v1

def sample_export_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    feature_selector = aiplatform_v1.FeatureSelector()
    feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']

    request = aiplatform_v1.ExportFeatureValuesRequest(
        entity_type="entity_type_value",
        destination=destination,
        feature_selector=feature_selector,
    )

    # Make the request
    operation = client.export_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ExportFeatureValuesRequest, dict]

The request object. Request message for FeaturestoreService.ExportFeatureValues.
entity_type str

Required. The resource name of the EntityType from which to export Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be ExportFeatureValuesResponse Response message for FeaturestoreService.ExportFeatureValues.

feature_path

feature_path(
    project: str, location: str, featurestore: str, entity_type: str, feature: str
)

Returns a fully-qualified feature string.

featurestore_path

featurestore_path(project: str, location: str, featurestore: str)

Returns a fully-qualified featurestore string.

from_service_account_file

from_service_account_file(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
FeaturestoreServiceClient The constructed client.

from_service_account_info

from_service_account_info(info: dict, *args, **kwargs)

Creates an instance of this client using the provided credentials info.

Parameter
Name Description
info dict

The service account private key info.
Returns
Type Description
FeaturestoreServiceClient The constructed client.

from_service_account_json

from_service_account_json(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
FeaturestoreServiceClient The constructed client.

get_entity_type

get_entity_type(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.GetEntityTypeRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets details of a single EntityType.

from google.cloud import aiplatform_v1

def sample_get_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetEntityTypeRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_entity_type(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetEntityTypeRequest, dict]

The request object. Request message for FeaturestoreService.GetEntityType.
name str

Required. The name of the EntityType resource. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.EntityType An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

get_feature

get_feature(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.GetFeatureRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets details of a single Feature.

from google.cloud import aiplatform_v1

def sample_get_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeatureRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_feature(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetFeatureRequest, dict]

The request object. Request message for FeaturestoreService.GetFeature.
name str

Required. The name of the Feature resource. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.Feature Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple.

get_featurestore

get_featurestore(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.GetFeaturestoreRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets details of a single Featurestore.

from google.cloud import aiplatform_v1

def sample_get_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeaturestoreRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_featurestore(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetFeaturestoreRequest, dict]

The request object. Request message for FeaturestoreService.GetFeaturestore.
name str

Required. The name of the Featurestore resource. This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.Featurestore 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.

get_mtls_endpoint_and_cert_source

get_mtls_endpoint_and_cert_source(
    client_options: Optional[google.api_core.client_options.ClientOptions] = None,
)

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variabel is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameter
Name Description
client_options google.api_core.client_options.ClientOptions

Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If any errors happen.
Returns
Type Description
Tuple[str, Callable[[], Tuple[bytes, bytes]]] returns the API endpoint and the client cert source to use.

import_feature_values

import_feature_values(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.ImportFeatureValuesRequest, dict]] = None, *, entity_type: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Imports Feature values into the Featurestore from a source storage. The progress of the import is tracked by the returned operation. The imported features are guaranteed to be visible to subsequent read operations after the operation is marked as successfully done. If an import operation fails, the Feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same import request again and wait till the new operation returned is marked as successfully done. There are also scenarios where the caller can cause inconsistency.

  • Source data for import contains multiple distinct Feature values for the same entity ID and timestamp.
  • Source is modified during an import. This includes adding, updating, or removing source data and/or metadata. Examples of updating metadata include but are not limited to changing storage location, storage class, or retention policy.
  • Online serving cluster is under-provisioned.
from google.cloud import aiplatform_v1

def sample_import_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    avro_source = aiplatform_v1.AvroSource()
    avro_source.gcs_source.uris = ['uris_value_1', 'uris_value_2']

    feature_specs = aiplatform_v1.FeatureSpec()
    feature_specs.id = "id_value"

    request = aiplatform_v1.ImportFeatureValuesRequest(
        avro_source=avro_source,
        feature_time_field="feature_time_field_value",
        entity_type="entity_type_value",
        feature_specs=feature_specs,
    )

    # Make the request
    operation = client.import_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ImportFeatureValuesRequest, dict]

The request object. Request message for FeaturestoreService.ImportFeatureValues.
entity_type str

Required. The resource name of the EntityType grouping the Features for which values are being imported. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType} This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be ImportFeatureValuesResponse Response message for FeaturestoreService.ImportFeatureValues.

list_entity_types

list_entity_types(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.ListEntityTypesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists EntityTypes in a given Featurestore.

from google.cloud import aiplatform_v1

def sample_list_entity_types():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListEntityTypesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_entity_types(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListEntityTypesRequest, dict]

The request object. Request message for FeaturestoreService.ListEntityTypes.
parent str

Required. The resource name of the Featurestore to list EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListEntityTypesPager Response message for FeaturestoreService.ListEntityTypes. Iterating over this object will yield results and resolve additional pages automatically.

list_features

list_features(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.ListFeaturesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists Features in a given EntityType.

from google.cloud import aiplatform_v1

def sample_list_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_features(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListFeaturesRequest, dict]

The request object. Request message for FeaturestoreService.ListFeatures.
parent str

Required. The resource name of the Location to list Features. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListFeaturesPager Response message for FeaturestoreService.ListFeatures. Iterating over this object will yield results and resolve additional pages automatically.

list_featurestores

list_featurestores(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.ListFeaturestoresRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists Featurestores in a given project and location.

from google.cloud import aiplatform_v1

def sample_list_featurestores():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturestoresRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_featurestores(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListFeaturestoresRequest, dict]

The request object. Request message for FeaturestoreService.ListFeaturestores.
parent str

Required. The resource name of the Location to list Featurestores. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.featurestore_service.pagers.ListFeaturestoresPager Response message for FeaturestoreService.ListFeaturestores. Iterating over this object will yield results and resolve additional pages automatically.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_entity_type_path

parse_entity_type_path(path: str)

Parses a entity_type path into its component segments.

parse_feature_path

parse_feature_path(path: str)

Parses a feature path into its component segments.

parse_featurestore_path

parse_featurestore_path(path: str)

Parses a featurestore path into its component segments.

search_features

search_features(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.SearchFeaturesRequest, dict]] = None, *, location: Optional[str] = None, query: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Searches Features matching a query in a given project.

from google.cloud import aiplatform_v1

def sample_search_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.SearchFeaturesRequest(
        location="location_value",
    )

    # Make the request
    page_result = client.search_features(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.SearchFeaturesRequest, dict]

The request object. Request message for FeaturestoreService.SearchFeatures.
location str

Required. The resource name of the Location to search Features. Format: projects/{project}/locations/{location} This corresponds to the location field on the request instance; if request is provided, this should not be set.
query str

Query string that is a conjunction of field-restricted queries and/or field-restricted filters. Field-restricted queries and filters can be combined using AND to form a conjunction. A field query is in the form FIELD:QUERY. This implicitly checks if QUERY exists as a substring within Feature's FIELD. The QUERY and the FIELD are converted to a sequence of words (i.e. tokens) for comparison. This is done by: - Removing leading/trailing whitespace and tokenizing the search value. Characters that are not one of alphanumeric [a-zA-Z0-9], underscore _, or asterisk * are treated as delimiters for tokens. * is treated as a wildcard that matches characters within a token. - Ignoring case. - Prepending an asterisk to the first and appending an asterisk to the last token in QUERY. A QUERY must be either a singular token or a phrase. A phrase is one or multiple words enclosed in double quotation marks ("). With phrases, the order of the words is important. Words in the phrase must be matching in order and consecutively. Supported FIELDs for field-restricted queries: - feature_id - description - entity_type_id Examples: - feature_id: foo --> Matches a Feature with ID containing the substring foo (eg. foo, foofeature, barfoo). - feature_id: foo*feature --> Matches a Feature with ID containing the substring foo*feature (eg. foobarfeature). - feature_id: foo AND description: bar --> Matches a Feature with ID containing the substring foo and description containing the substring bar. Besides field queries, the following exact-match filters are supported. The exact-match filters do not support wildcards. Unlike field-restricted queries, exact-match filters are case-sensitive. - feature_id: Supports = comparisons. - description: Supports = comparisons. Multi-token filters should be enclosed in quotes. - entity_type_id: Supports = comparisons. - value_type: Supports = and != comparisons. - labels: Supports key-value equality as well as key presence. - featurestore_id: Supports = comparisons. Examples: - description = "foo bar" --> Any Feature with description exactly equal to foo bar - value_type = DOUBLE --> Features whose type is DOUBLE. - labels.active = yes AND labels.env = prod --> Features having both (active: yes) and (env: prod) labels. - labels.env: * --> Any Feature which has a label with env as the key. This corresponds to the query field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.featurestore_service.pagers.SearchFeaturesPager Response message for FeaturestoreService.SearchFeatures. Iterating over this object will yield results and resolve additional pages automatically.

update_entity_type

update_entity_type(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.UpdateEntityTypeRequest, dict]] = None, *, entity_type: Optional[google.cloud.aiplatform_v1.types.entity_type.EntityType] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates the parameters of a single EntityType.

from google.cloud import aiplatform_v1

def sample_update_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateEntityTypeRequest(
    )

    # Make the request
    response = client.update_entity_type(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateEntityTypeRequest, dict]

The request object. Request message for FeaturestoreService.UpdateEntityType.
entity_type google.cloud.aiplatform_v1.types.EntityType

Required. The EntityType's name field is used to identify the EntityType to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} This corresponds to the entity_type field on the request instance; if request is provided, this should not be set.
update_mask google.protobuf.field_mask_pb2.FieldMask

Field mask is used to specify the fields to be overwritten in the EntityType resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields. Updatable fields: - description - labels - monitoring_config.snapshot_analysis.disabled - monitoring_config.snapshot_analysis.monitoring_interval This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.EntityType An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

update_feature

update_feature(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.UpdateFeatureRequest, dict]] = None, *, feature: Optional[google.cloud.aiplatform_v1.types.feature.Feature] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates the parameters of a single Feature.

from google.cloud import aiplatform_v1

def sample_update_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    feature = aiplatform_v1.Feature()
    feature.value_type = "BYTES"

    request = aiplatform_v1.UpdateFeatureRequest(
        feature=feature,
    )

    # Make the request
    response = client.update_feature(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateFeatureRequest, dict]

The request object. Request message for FeaturestoreService.UpdateFeature.
feature google.cloud.aiplatform_v1.types.Feature

Required. The Feature's name field is used to identify the Feature to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} This corresponds to the feature field on the request instance; if request is provided, this should not be set.
update_mask google.protobuf.field_mask_pb2.FieldMask

Field mask is used to specify the fields to be overwritten in the Features resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields. Updatable fields: - description - labels - monitoring_config.snapshot_analysis.disabled - monitoring_config.snapshot_analysis.monitoring_interval This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.Feature Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple.

update_featurestore

update_featurestore(request: Optional[Union[google.cloud.aiplatform_v1.types.featurestore_service.UpdateFeaturestoreRequest, dict]] = None, *, featurestore: Optional[google.cloud.aiplatform_v1.types.featurestore.Featurestore] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates the parameters of a single Featurestore.

from google.cloud import aiplatform_v1

def sample_update_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateFeaturestoreRequest(
    )

    # Make the request
    operation = client.update_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateFeaturestoreRequest, dict]

The request object. Request message for FeaturestoreService.UpdateFeaturestore.
featurestore google.cloud.aiplatform_v1.types.Featurestore

Required. The Featurestore's name field is used to identify the Featurestore to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore} This corresponds to the featurestore field on the request instance; if request is provided, this should not be set.
update_mask google.protobuf.field_mask_pb2.FieldMask

Field mask is used to specify the fields to be overwritten in the Featurestore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields. Updatable fields: - labels - online_serving_config.fixed_node_count This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be Featurestore 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.