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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/v1.getFeatureGroupFeature

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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

    Gets details of a single Feature.

    Using getFeatureGroupFeature

    Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

    function getFeatureGroupFeature(args: GetFeatureGroupFeatureArgs, opts?: InvokeOptions): Promise<GetFeatureGroupFeatureResult>
function getFeatureGroupFeatureOutput(args: GetFeatureGroupFeatureOutputArgs, opts?: InvokeOptions): Output<GetFeatureGroupFeatureResult>
    def get_feature_group_feature(feature_group_id: Optional[str] = None,
                              feature_id: Optional[str] = None,
                              location: Optional[str] = None,
                              project: Optional[str] = None,
                              opts: Optional[InvokeOptions] = None) -> GetFeatureGroupFeatureResult
def get_feature_group_feature_output(feature_group_id: Optional[pulumi.Input[str]] = None,
                              feature_id: Optional[pulumi.Input[str]] = None,
                              location: Optional[pulumi.Input[str]] = None,
                              project: Optional[pulumi.Input[str]] = None,
                              opts: Optional[InvokeOptions] = None) -> Output[GetFeatureGroupFeatureResult]
    func LookupFeatureGroupFeature(ctx *Context, args *LookupFeatureGroupFeatureArgs, opts ...InvokeOption) (*LookupFeatureGroupFeatureResult, error)
func LookupFeatureGroupFeatureOutput(ctx *Context, args *LookupFeatureGroupFeatureOutputArgs, opts ...InvokeOption) LookupFeatureGroupFeatureResultOutput

    > Note: This function is named LookupFeatureGroupFeature in the Go SDK.

    public static class GetFeatureGroupFeature 
{
    public static Task<GetFeatureGroupFeatureResult> InvokeAsync(GetFeatureGroupFeatureArgs args, InvokeOptions? opts = null)
    public static Output<GetFeatureGroupFeatureResult> Invoke(GetFeatureGroupFeatureInvokeArgs args, InvokeOptions? opts = null)
}
    public static CompletableFuture<GetFeatureGroupFeatureResult> getFeatureGroupFeature(GetFeatureGroupFeatureArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

    fn::invoke:
  function: google-native:aiplatform/v1:getFeatureGroupFeature
  arguments:
    # arguments dictionary

    The following arguments are supported:

    FeatureGroupId string
    FeatureId string
    Location string
    Project string
    FeatureGroupId string
    FeatureId string
    Location string
    Project string
    featureGroupId String
    featureId String
    location String
    project String
    featureGroupId string
    featureId string
    location string
    project string
    featureGroupId String
    featureId String
    location String
    project String

    getFeatureGroupFeature Result

    The following output properties are available:

    CreateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    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.
    MonitoringStatsAnomalies List<Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1FeatureMonitoringStatsAnomalyResponse>
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    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.
    UpdateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    ValueType string
    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.
    CreateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    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.
    MonitoringStatsAnomalies []GoogleCloudAiplatformV1FeatureMonitoringStatsAnomalyResponse
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    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.
    UpdateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    ValueType string
    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.
    createTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    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.
    monitoringStatsAnomalies List<GoogleCloudAiplatformV1FeatureMonitoringStatsAnomalyResponse>
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    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.
    updateTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    valueType String
    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.
    createTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    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.
    monitoringStatsAnomalies GoogleCloudAiplatformV1FeatureMonitoringStatsAnomalyResponse[]
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    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.
    updateTime string
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    valueType string
    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.
    create_time str
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    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.
    monitoring_stats_anomalies Sequence[GoogleCloudAiplatformV1FeatureMonitoringStatsAnomalyResponse]
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    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.
    update_time str
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    value_type str
    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.
    createTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
    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.
    monitoringStatsAnomalies List<Property Map>
    Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
    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.
    updateTime String
    Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
    valueType String
    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.

    Supporting Types

    GoogleCloudAiplatformV1FeatureMonitoringStatsAnomalyResponse

    FeatureStatsAnomaly Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    Objective string
    The objective for each stats.
    FeatureStatsAnomaly GoogleCloudAiplatformV1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    Objective string
    The objective for each stats.
    featureStatsAnomaly GoogleCloudAiplatformV1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    objective String
    The objective for each stats.
    featureStatsAnomaly GoogleCloudAiplatformV1FeatureStatsAnomalyResponse
    The stats and anomalies generated at specific timestamp.
    objective string
    The objective for each stats.
    feature_stats_anomaly GoogleCloudAiplatformV1FeatureStatsAnomalyResponse
    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.

    GoogleCloudAiplatformV1FeatureStatsAnomalyResponse

    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.

    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