Google Cloud Native v0.32.0, Nov 29 23

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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.getFeatureStoreFeature

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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

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Using getFeatureStoreFeature

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 getFeatureStoreFeature(args: GetFeatureStoreFeatureArgs, opts?: InvokeOptions): Promise<GetFeatureStoreFeatureResult>
function getFeatureStoreFeatureOutput(args: GetFeatureStoreFeatureOutputArgs, opts?: InvokeOptions): Output<GetFeatureStoreFeatureResult>

def get_feature_store_feature(entity_type_id: Optional[str] = None,
                              feature_id: Optional[str] = None,
                              featurestore_id: Optional[str] = None,
                              location: Optional[str] = None,
                              project: Optional[str] = None,
                              opts: Optional[InvokeOptions] = None) -> GetFeatureStoreFeatureResult
def get_feature_store_feature_output(entity_type_id: Optional[pulumi.Input[str]] = None,
                              feature_id: Optional[pulumi.Input[str]] = None,
                              featurestore_id: Optional[pulumi.Input[str]] = None,
                              location: Optional[pulumi.Input[str]] = None,
                              project: Optional[pulumi.Input[str]] = None,
                              opts: Optional[InvokeOptions] = None) -> Output[GetFeatureStoreFeatureResult]

func LookupFeatureStoreFeature(ctx *Context, args *LookupFeatureStoreFeatureArgs, opts ...InvokeOption) (*LookupFeatureStoreFeatureResult, error)
func LookupFeatureStoreFeatureOutput(ctx *Context, args *LookupFeatureStoreFeatureOutputArgs, opts ...InvokeOption) LookupFeatureStoreFeatureResultOutput

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

public static class GetFeatureStoreFeature 
{
    public static Task<GetFeatureStoreFeatureResult> InvokeAsync(GetFeatureStoreFeatureArgs args, InvokeOptions? opts = null)
    public static Output<GetFeatureStoreFeatureResult> Invoke(GetFeatureStoreFeatureInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetFeatureStoreFeatureResult> getFeatureStoreFeature(GetFeatureStoreFeatureArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

fn::invoke:
  function: google-native:aiplatform/v1beta1:getFeatureStoreFeature
  arguments:
    # arguments dictionary

The following arguments are supported:

EntityTypeId string
FeatureId string
FeaturestoreId string
Location string
Project string

EntityTypeId string
FeatureId string
FeaturestoreId string
Location string
Project string

entityTypeId String
featureId String
featurestoreId String
location String
project String

entityTypeId string
featureId string
featurestoreId string
location string
project string

entity_type_id str
feature_id str
featurestore_id str
location str
project str

entityTypeId String
featureId String
featurestoreId String
location String
project String

getFeatureStoreFeature 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.
MonitoringConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse: 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.
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.
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.
MonitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse: 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.
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.
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.
monitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse: 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.
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.
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.
monitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse: 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.
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.
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_config GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse: 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.
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.
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.
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.
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.
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

GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse

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

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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse

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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse

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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse

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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse

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 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

google-native.aiplatform/v1beta1.getFeatureStoreFeature

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Using getFeatureStoreFeature

getFeatureStoreFeature Result

Supporting Types

GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse

GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse

Package Details

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