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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

google-native.aiplatform/v1beta1.FeatureStoreFeature

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

    Creates a new Feature in a given EntityType.

    Create FeatureStoreFeature Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new FeatureStoreFeature(name: string, args: FeatureStoreFeatureArgs, opts?: CustomResourceOptions);
    @overload
def FeatureStoreFeature(resource_name: str,
                        args: FeatureStoreFeatureArgs,
                        opts: Optional[ResourceOptions] = None)

@overload
def FeatureStoreFeature(resource_name: str,
                        opts: Optional[ResourceOptions] = None,
                        feature_id: Optional[str] = None,
                        featurestore_id: Optional[str] = None,
                        entity_type_id: Optional[str] = None,
                        labels: Optional[Mapping[str, str]] = None,
                        etag: Optional[str] = None,
                        disable_monitoring: Optional[bool] = None,
                        description: Optional[str] = None,
                        location: Optional[str] = None,
                        monitoring_config: Optional[GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigArgs] = None,
                        name: Optional[str] = None,
                        project: Optional[str] = None,
                        value_type: Optional[FeatureStoreFeatureValueType] = None,
                        version_column_name: Optional[str] = None)
    func NewFeatureStoreFeature(ctx *Context, name string, args FeatureStoreFeatureArgs, opts ...ResourceOption) (*FeatureStoreFeature, error)
    public FeatureStoreFeature(string name, FeatureStoreFeatureArgs args, CustomResourceOptions? opts = null)
    public FeatureStoreFeature(String name, FeatureStoreFeatureArgs args)
public FeatureStoreFeature(String name, FeatureStoreFeatureArgs args, CustomResourceOptions options)

    type: google-native:aiplatform/v1beta1:FeatureStoreFeature
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args FeatureStoreFeatureArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args FeatureStoreFeatureArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args FeatureStoreFeatureArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args FeatureStoreFeatureArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args FeatureStoreFeatureArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var google_nativeFeatureStoreFeatureResource = new GoogleNative.Aiplatform.V1Beta1.FeatureStoreFeature("google-nativeFeatureStoreFeatureResource", new()
{
    FeatureId = "string",
    FeaturestoreId = "string",
    EntityTypeId = "string",
    Labels = 
    {
        { "string", "string" },
    },
    Etag = "string",
    DisableMonitoring = false,
    Description = "string",
    Location = "string",
    Name = "string",
    Project = "string",
    ValueType = GoogleNative.Aiplatform.V1Beta1.FeatureStoreFeatureValueType.ValueTypeUnspecified,
    VersionColumnName = "string",
});

    example, err := aiplatformv1beta1.NewFeatureStoreFeature(ctx, "google-nativeFeatureStoreFeatureResource", &aiplatformv1beta1.FeatureStoreFeatureArgs{
FeatureId: pulumi.String("string"),
FeaturestoreId: pulumi.String("string"),
EntityTypeId: pulumi.String("string"),
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Etag: pulumi.String("string"),
DisableMonitoring: pulumi.Bool(false),
Description: pulumi.String("string"),
Location: pulumi.String("string"),
Name: pulumi.String("string"),
Project: pulumi.String("string"),
ValueType: aiplatformv1beta1.FeatureStoreFeatureValueTypeValueTypeUnspecified,
VersionColumnName: pulumi.String("string"),
})

    var google_nativeFeatureStoreFeatureResource = new FeatureStoreFeature("google-nativeFeatureStoreFeatureResource", FeatureStoreFeatureArgs.builder()
    .featureId("string")
    .featurestoreId("string")
    .entityTypeId("string")
    .labels(Map.of("string", "string"))
    .etag("string")
    .disableMonitoring(false)
    .description("string")
    .location("string")
    .name("string")
    .project("string")
    .valueType("VALUE_TYPE_UNSPECIFIED")
    .versionColumnName("string")
    .build());

    google_native_feature_store_feature_resource = google_native.aiplatform.v1beta1.FeatureStoreFeature("google-nativeFeatureStoreFeatureResource",
    feature_id="string",
    featurestore_id="string",
    entity_type_id="string",
    labels={
        "string": "string",
    },
    etag="string",
    disable_monitoring=False,
    description="string",
    location="string",
    name="string",
    project="string",
    value_type=google_native.aiplatform.v1beta1.FeatureStoreFeatureValueType.VALUE_TYPE_UNSPECIFIED,
    version_column_name="string")

    const google_nativeFeatureStoreFeatureResource = new google_native.aiplatform.v1beta1.FeatureStoreFeature("google-nativeFeatureStoreFeatureResource", {
    featureId: "string",
    featurestoreId: "string",
    entityTypeId: "string",
    labels: {
        string: "string",
    },
    etag: "string",
    disableMonitoring: false,
    description: "string",
    location: "string",
    name: "string",
    project: "string",
    valueType: google_native.aiplatform.v1beta1.FeatureStoreFeatureValueType.ValueTypeUnspecified,
    versionColumnName: "string",
});

    type: google-native:aiplatform/v1beta1:FeatureStoreFeature
properties:
    description: string
    disableMonitoring: false
    entityTypeId: string
    etag: string
    featureId: string
    featurestoreId: string
    labels:
        string: string
    location: string
    name: string
    project: string
    valueType: VALUE_TYPE_UNSPECIFIED
    versionColumnName: string

    FeatureStoreFeature Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    The FeatureStoreFeature resource accepts the following input properties:

    EntityTypeId string
    FeatureId string
    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 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
    FeaturestoreId string
    Description string
    Description of the Feature.
    DisableMonitoring bool
    Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
    Etag string
    Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    Labels Dictionary<string, string>
    Optional. The labels with user-defined metadata to organize your Features. 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 Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    Location string
    MonitoringConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig
    Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Deprecated: Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Name string
    Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
    Project string
    ValueType Pulumi.GoogleNative.Aiplatform.V1Beta1.FeatureStoreFeatureValueType
    Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
    VersionColumnName string
    Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View columnn hosting data for this version. If no value is provided, will use feature_id.
    EntityTypeId string
    FeatureId string
    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 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
    FeaturestoreId string
    Description string
    Description of the Feature.
    DisableMonitoring bool
    Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
    Etag string
    Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    Labels map[string]string
    Optional. The labels with user-defined metadata to organize your Features. 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 Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    Location string
    MonitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigArgs
    Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Deprecated: Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Name string
    Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
    Project string
    ValueType FeatureStoreFeatureValueType
    Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
    VersionColumnName string
    Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View columnn hosting data for this version. If no value is provided, will use feature_id.
    entityTypeId String
    featureId String
    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 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
    featurestoreId String
    description String
    Description of the Feature.
    disableMonitoring Boolean
    Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
    etag String
    Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    labels Map<String,String>
    Optional. The labels with user-defined metadata to organize your Features. 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 Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location String
    monitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig
    Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Deprecated: Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    name String
    Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
    project String
    valueType FeatureStoreFeatureValueType
    Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
    versionColumnName String
    Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View columnn hosting data for this version. If no value is provided, will use feature_id.
    entityTypeId string
    featureId string
    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 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
    featurestoreId string
    description string
    Description of the Feature.
    disableMonitoring boolean
    Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
    etag string
    Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    labels {[key: string]: string}
    Optional. The labels with user-defined metadata to organize your Features. 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 Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location string
    monitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig
    Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Deprecated: Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    name string
    Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
    project string
    valueType FeatureStoreFeatureValueType
    Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
    versionColumnName string
    Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View columnn hosting data for this version. If no value is provided, will use feature_id.
    entity_type_id str
    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 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
    featurestore_id str
    description str
    Description of the Feature.
    disable_monitoring bool
    Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
    etag str
    Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    labels Mapping[str, str]
    Optional. The labels with user-defined metadata to organize your Features. 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 Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location str
    monitoring_config GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigArgs
    Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Deprecated: Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    name str
    Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
    project str
    value_type FeatureStoreFeatureValueType
    Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
    version_column_name str
    Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View columnn hosting data for this version. If no value is provided, will use feature_id.
    entityTypeId String
    featureId String
    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 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
    featurestoreId String
    description String
    Description of the Feature.
    disableMonitoring Boolean
    Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
    etag String
    Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
    labels Map<String>
    Optional. The labels with user-defined metadata to organize your Features. 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 Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    location String
    monitoringConfig Property Map
    Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    Deprecated: Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

    name String
    Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
    project String
    valueType "VALUE_TYPE_UNSPECIFIED" | "BOOL" | "BOOL_ARRAY" | "DOUBLE" | "DOUBLE_ARRAY" | "INT64" | "INT64_ARRAY" | "STRING" | "STRING_ARRAY" | "BYTES"
    Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
    versionColumnName String
    Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View columnn hosting data for this version. If no value is provided, will use feature_id.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the FeatureStoreFeature resource produces the following output properties:

    CreateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    MonitoringStats List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse>
    Only applicable for Vertex AI Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
    MonitoringStatsAnomalies List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse>
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    UpdateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    CreateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    MonitoringStats []GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    Only applicable for Vertex AI Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
    MonitoringStatsAnomalies []GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    UpdateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    createTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    id String
    The provider-assigned unique ID for this managed resource.
    monitoringStats List<GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse>
    Only applicable for Vertex AI Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
    monitoringStatsAnomalies List<GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse>
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    updateTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    createTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    id string
    The provider-assigned unique ID for this managed resource.
    monitoringStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse[]
    Only applicable for Vertex AI Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
    monitoringStatsAnomalies GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse[]
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    updateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    create_time str
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    id str
    The provider-assigned unique ID for this managed resource.
    monitoring_stats Sequence[GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse]
    Only applicable for Vertex AI Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
    monitoring_stats_anomalies Sequence[GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse]
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    update_time str
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    createTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    id String
    The provider-assigned unique ID for this managed resource.
    monitoringStats List<Property Map>
    Only applicable for Vertex AI Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
    monitoringStatsAnomalies List<Property Map>
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    updateTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.

    Supporting Types

    FeatureStoreFeatureValueType, FeatureStoreFeatureValueTypeArgs

    ValueTypeUnspecified
    VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
    Bool
    BOOLUsed for Feature that is a boolean.
    BoolArray
    BOOL_ARRAYUsed for Feature that is a list of boolean.
    Double
    DOUBLEUsed for Feature that is double.
    DoubleArray
    DOUBLE_ARRAYUsed for Feature that is a list of double.
    Int64
    INT64Used for Feature that is INT64.
    Int64Array
    INT64_ARRAYUsed for Feature that is a list of INT64.
    String
    STRINGUsed for Feature that is string.
    StringArray
    STRING_ARRAYUsed for Feature that is a list of String.
    Bytes
    BYTESUsed for Feature that is bytes.
    FeatureStoreFeatureValueTypeValueTypeUnspecified
    VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
    FeatureStoreFeatureValueTypeBool
    BOOLUsed for Feature that is a boolean.
    FeatureStoreFeatureValueTypeBoolArray
    BOOL_ARRAYUsed for Feature that is a list of boolean.
    FeatureStoreFeatureValueTypeDouble
    DOUBLEUsed for Feature that is double.
    FeatureStoreFeatureValueTypeDoubleArray
    DOUBLE_ARRAYUsed for Feature that is a list of double.
    FeatureStoreFeatureValueTypeInt64
    INT64Used for Feature that is INT64.
    FeatureStoreFeatureValueTypeInt64Array
    INT64_ARRAYUsed for Feature that is a list of INT64.
    FeatureStoreFeatureValueTypeString
    STRINGUsed for Feature that is string.
    FeatureStoreFeatureValueTypeStringArray
    STRING_ARRAYUsed for Feature that is a list of String.
    FeatureStoreFeatureValueTypeBytes
    BYTESUsed for Feature that is bytes.
    ValueTypeUnspecified
    VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
    Bool
    BOOLUsed for Feature that is a boolean.
    BoolArray
    BOOL_ARRAYUsed for Feature that is a list of boolean.
    Double
    DOUBLEUsed for Feature that is double.
    DoubleArray
    DOUBLE_ARRAYUsed for Feature that is a list of double.
    Int64
    INT64Used for Feature that is INT64.
    Int64Array
    INT64_ARRAYUsed for Feature that is a list of INT64.
    String
    STRINGUsed for Feature that is string.
    StringArray
    STRING_ARRAYUsed for Feature that is a list of String.
    Bytes
    BYTESUsed for Feature that is bytes.
    ValueTypeUnspecified
    VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
    Bool
    BOOLUsed for Feature that is a boolean.
    BoolArray
    BOOL_ARRAYUsed for Feature that is a list of boolean.
    Double
    DOUBLEUsed for Feature that is double.
    DoubleArray
    DOUBLE_ARRAYUsed for Feature that is a list of double.
    Int64
    INT64Used for Feature that is INT64.
    Int64Array
    INT64_ARRAYUsed for Feature that is a list of INT64.
    String
    STRINGUsed for Feature that is string.
    StringArray
    STRING_ARRAYUsed for Feature that is a list of String.
    Bytes
    BYTESUsed for Feature that is bytes.
    VALUE_TYPE_UNSPECIFIED
    VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
    BOOL
    BOOLUsed for Feature that is a boolean.
    BOOL_ARRAY
    BOOL_ARRAYUsed for Feature that is a list of boolean.
    DOUBLE
    DOUBLEUsed for Feature that is double.
    DOUBLE_ARRAY
    DOUBLE_ARRAYUsed for Feature that is a list of double.
    INT64
    INT64Used for Feature that is INT64.
    INT64_ARRAY
    INT64_ARRAYUsed for Feature that is a list of INT64.
    STRING
    STRINGUsed for Feature that is string.
    STRING_ARRAY
    STRING_ARRAYUsed for Feature that is a list of String.
    BYTES
    BYTESUsed for Feature that is bytes.
    "VALUE_TYPE_UNSPECIFIED"
    VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
    "BOOL"
    BOOLUsed for Feature that is a boolean.
    "BOOL_ARRAY"
    BOOL_ARRAYUsed for Feature that is a list of boolean.
    "DOUBLE"
    DOUBLEUsed for Feature that is double.
    "DOUBLE_ARRAY"
    DOUBLE_ARRAYUsed for Feature that is a list of double.
    "INT64"
    INT64Used for Feature that is INT64.
    "INT64_ARRAY"
    INT64_ARRAYUsed for Feature that is a list of INT64.
    "STRING"
    STRINGUsed for Feature that is string.
    "STRING_ARRAY"
    STRING_ARRAYUsed for Feature that is a list of String.
    "BYTES"
    BYTESUsed for Feature that is bytes.

    GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse, GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponseArgs

    FeatureStatsAnomaly Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    Objective string
    The objective for each stats.
    FeatureStatsAnomaly GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    Objective string
    The objective for each stats.
    featureStatsAnomaly GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    objective String
    The objective for each stats.
    featureStatsAnomaly GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    objective string
    The objective for each stats.
    feature_stats_anomaly GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    objective str
    The objective for each stats.
    featureStatsAnomaly Property Map
    The stats and anomalies generated at specific timestamp.
    objective String
    The objective for each stats.

    GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse, GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponseArgs

    AnomalyDetectionThreshold double
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    AnomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    DistributionDeviation double
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    EndTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    Score double
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    StartTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    StatsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    AnomalyDetectionThreshold float64
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    AnomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    DistributionDeviation float64
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    EndTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    Score float64
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    StartTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    StatsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold Double
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri String
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation Double
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime String
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score Double
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime String
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri String
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold number
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation number
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score number
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomaly_detection_threshold float
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomaly_uri str
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distribution_deviation float
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    end_time str
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score float
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    start_time str
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    stats_uri str
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold Number
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri String
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation Number
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime String
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score Number
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime String
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri String
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigArgs

    CategoricalThresholdConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    ImportFeaturesAnalysis Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis
    The config for ImportFeatures Analysis Based Feature Monitoring.
    NumericalThresholdConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    SnapshotAnalysis Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis
    The config for Snapshot Analysis Based Feature Monitoring.
    CategoricalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    ImportFeaturesAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis
    The config for ImportFeatures Analysis Based Feature Monitoring.
    NumericalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    SnapshotAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis
    The config for Snapshot Analysis Based Feature Monitoring.
    categoricalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    importFeaturesAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numericalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshotAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis
    The config for Snapshot Analysis Based Feature Monitoring.
    categoricalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    importFeaturesAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numericalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshotAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis
    The config for Snapshot Analysis Based Feature Monitoring.
    categorical_threshold_config GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    import_features_analysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numerical_threshold_config GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshot_analysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis
    The config for Snapshot Analysis Based Feature Monitoring.
    categoricalThresholdConfig Property Map
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    importFeaturesAnalysis Property Map
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numericalThresholdConfig Property Map
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshotAnalysis Property Map
    The config for Snapshot Analysis Based Feature Monitoring.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisArgs

    AnomalyDetectionBaseline GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaseline
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    State GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisState
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomalyDetectionBaseline GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaseline
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisState
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomalyDetectionBaseline GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaseline
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisState
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomaly_detection_baseline GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaseline
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisState
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomalyDetectionBaseline "BASELINE_UNSPECIFIED" | "LATEST_STATS" | "MOST_RECENT_SNAPSHOT_STATS" | "PREVIOUS_IMPORT_FEATURES_STATS"
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state "STATE_UNSPECIFIED" | "DEFAULT" | "ENABLED" | "DISABLED"
    Whether to enable / disable / inherite default hebavior for import features analysis.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaseline, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaselineArgs

    BaselineUnspecified
    BASELINE_UNSPECIFIEDShould not be used.
    LatestStats
    LATEST_STATSChoose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
    MostRecentSnapshotStats
    MOST_RECENT_SNAPSHOT_STATSUse the statistics generated by the most recent snapshot analysis if exists.
    PreviousImportFeaturesStats
    PREVIOUS_IMPORT_FEATURES_STATSUse the statistics generated by the previous import features analysis if exists.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaselineBaselineUnspecified
    BASELINE_UNSPECIFIEDShould not be used.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaselineLatestStats
    LATEST_STATSChoose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaselineMostRecentSnapshotStats
    MOST_RECENT_SNAPSHOT_STATSUse the statistics generated by the most recent snapshot analysis if exists.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaselinePreviousImportFeaturesStats
    PREVIOUS_IMPORT_FEATURES_STATSUse the statistics generated by the previous import features analysis if exists.
    BaselineUnspecified
    BASELINE_UNSPECIFIEDShould not be used.
    LatestStats
    LATEST_STATSChoose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
    MostRecentSnapshotStats
    MOST_RECENT_SNAPSHOT_STATSUse the statistics generated by the most recent snapshot analysis if exists.
    PreviousImportFeaturesStats
    PREVIOUS_IMPORT_FEATURES_STATSUse the statistics generated by the previous import features analysis if exists.
    BaselineUnspecified
    BASELINE_UNSPECIFIEDShould not be used.
    LatestStats
    LATEST_STATSChoose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
    MostRecentSnapshotStats
    MOST_RECENT_SNAPSHOT_STATSUse the statistics generated by the most recent snapshot analysis if exists.
    PreviousImportFeaturesStats
    PREVIOUS_IMPORT_FEATURES_STATSUse the statistics generated by the previous import features analysis if exists.
    BASELINE_UNSPECIFIED
    BASELINE_UNSPECIFIEDShould not be used.
    LATEST_STATS
    LATEST_STATSChoose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
    MOST_RECENT_SNAPSHOT_STATS
    MOST_RECENT_SNAPSHOT_STATSUse the statistics generated by the most recent snapshot analysis if exists.
    PREVIOUS_IMPORT_FEATURES_STATS
    PREVIOUS_IMPORT_FEATURES_STATSUse the statistics generated by the previous import features analysis if exists.
    "BASELINE_UNSPECIFIED"
    BASELINE_UNSPECIFIEDShould not be used.
    "LATEST_STATS"
    LATEST_STATSChoose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
    "MOST_RECENT_SNAPSHOT_STATS"
    MOST_RECENT_SNAPSHOT_STATSUse the statistics generated by the most recent snapshot analysis if exists.
    "PREVIOUS_IMPORT_FEATURES_STATS"
    PREVIOUS_IMPORT_FEATURES_STATSUse the statistics generated by the previous import features analysis if exists.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponseArgs

    AnomalyDetectionBaseline string
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    State string
    Whether to enable / disable / inherite default hebavior for import features analysis.
    AnomalyDetectionBaseline string
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    State string
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomalyDetectionBaseline String
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state String
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomalyDetectionBaseline string
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state string
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomaly_detection_baseline str
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state str
    Whether to enable / disable / inherite default hebavior for import features analysis.
    anomalyDetectionBaseline String
    The baseline used to do anomaly detection for the statistics generated by import features analysis.
    state String
    Whether to enable / disable / inherite default hebavior for import features analysis.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisState, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisStateArgs

    StateUnspecified
    STATE_UNSPECIFIEDShould not be used.
    Default
    DEFAULTThe default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
    Enabled
    ENABLEDExplicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
    Disabled
    DISABLEDExplicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisStateStateUnspecified
    STATE_UNSPECIFIEDShould not be used.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisStateDefault
    DEFAULTThe default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisStateEnabled
    ENABLEDExplicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisStateDisabled
    DISABLEDExplicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.
    StateUnspecified
    STATE_UNSPECIFIEDShould not be used.
    Default
    DEFAULTThe default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
    Enabled
    ENABLEDExplicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
    Disabled
    DISABLEDExplicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.
    StateUnspecified
    STATE_UNSPECIFIEDShould not be used.
    Default
    DEFAULTThe default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
    Enabled
    ENABLEDExplicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
    Disabled
    DISABLEDExplicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.
    STATE_UNSPECIFIED
    STATE_UNSPECIFIEDShould not be used.
    DEFAULT
    DEFAULTThe default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
    ENABLED
    ENABLEDExplicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
    DISABLED
    DISABLEDExplicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.
    "STATE_UNSPECIFIED"
    STATE_UNSPECIFIEDShould not be used.
    "DEFAULT"
    DEFAULTThe default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
    "ENABLED"
    ENABLEDExplicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
    "DISABLED"
    DISABLEDExplicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponseArgs

    CategoricalThresholdConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    ImportFeaturesAnalysis Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
    The config for ImportFeatures Analysis Based Feature Monitoring.
    NumericalThresholdConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    SnapshotAnalysis Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
    The config for Snapshot Analysis Based Feature Monitoring.
    CategoricalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    ImportFeaturesAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
    The config for ImportFeatures Analysis Based Feature Monitoring.
    NumericalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    SnapshotAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
    The config for Snapshot Analysis Based Feature Monitoring.
    categoricalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    importFeaturesAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numericalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshotAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
    The config for Snapshot Analysis Based Feature Monitoring.
    categoricalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    importFeaturesAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numericalThresholdConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshotAnalysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
    The config for Snapshot Analysis Based Feature Monitoring.
    categorical_threshold_config GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    import_features_analysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numerical_threshold_config GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshot_analysis GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
    The config for Snapshot Analysis Based Feature Monitoring.
    categoricalThresholdConfig Property Map
    Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
    importFeaturesAnalysis Property Map
    The config for ImportFeatures Analysis Based Feature Monitoring.
    numericalThresholdConfig Property Map
    Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
    snapshotAnalysis Property Map
    The config for Snapshot Analysis Based Feature Monitoring.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisArgs

    Disabled bool
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    MonitoringInterval string
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    MonitoringIntervalDays int
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    StalenessDays int
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    Disabled bool
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    MonitoringInterval string
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    MonitoringIntervalDays int
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    StalenessDays int
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled Boolean
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoringInterval String
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoringIntervalDays Integer
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    stalenessDays Integer
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled boolean
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoringInterval string
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoringIntervalDays number
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    stalenessDays number
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled bool
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoring_interval str
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoring_interval_days int
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    staleness_days int
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled Boolean
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoringInterval String
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoringIntervalDays Number
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    stalenessDays Number
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponseArgs

    Disabled bool
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    MonitoringInterval string
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    MonitoringIntervalDays int
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    StalenessDays int
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    Disabled bool
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    MonitoringInterval string
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    MonitoringIntervalDays int
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    StalenessDays int
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled Boolean
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoringInterval String
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoringIntervalDays Integer
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    stalenessDays Integer
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled boolean
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoringInterval string
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoringIntervalDays number
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    stalenessDays number
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled bool
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoring_interval str
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoring_interval_days int
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    staleness_days int
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.
    disabled Boolean
    The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
    monitoringInterval String
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
    monitoringIntervalDays Number
    Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
    stalenessDays Number
    Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigArgs

    Value double
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    Value float64
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Double
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value number
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value float
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Number
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

    GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponseArgs

    Value double
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    Value float64
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Double
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value number
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value float
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Number
    Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

    Package Details

    Repository
    Google Cloud Native pulumi/pulumi-google-native
    License
    Apache-2.0
    google-native logo

    Google Cloud Native is in preview. Google Cloud Classic is fully supported.

    Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
    pulumi/pulumi-google-native