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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

google-native.aiplatform/v1beta1.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

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>
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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]
Copy
func LookupFeatureGroupFeature(ctx *Context, args *LookupFeatureGroupFeatureArgs, opts ...InvokeOption) (*LookupFeatureGroupFeatureResult, error)
func LookupFeatureGroupFeatureOutput(ctx *Context, args *LookupFeatureGroupFeatureOutputArgs, opts ...InvokeOption) LookupFeatureGroupFeatureResultOutput
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> 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)
}
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public static CompletableFuture<GetFeatureGroupFeatureResult> getFeatureGroupFeature(GetFeatureGroupFeatureArgs args, InvokeOptions options)
public static Output<GetFeatureGroupFeatureResult> getFeatureGroupFeature(GetFeatureGroupFeatureArgs args, InvokeOptions options)
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fn::invoke:
  function: google-native:aiplatform/v1beta1:getFeatureGroupFeature
  arguments:
    # arguments dictionary
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The following arguments are supported:

FeatureGroupId This property is required. string
FeatureId This property is required. string
Location This property is required. string
Project string
FeatureGroupId This property is required. string
FeatureId This property is required. string
Location This property is required. string
Project string
featureGroupId This property is required. String
featureId This property is required. String
location This property is required. String
project String
featureGroupId This property is required. string
featureId This property is required. string
location This property is required. string
project string
feature_group_id This property is required. str
feature_id This property is required. str
location This property is required. str
project str
featureGroupId This property is required. String
featureId This property is required. String
location This property is required. 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.
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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
The stats and anomalies generated at specific timestamp.
Objective This property is required. string
The objective for each stats.
FeatureStatsAnomaly This property is required. GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
The stats and anomalies generated at specific timestamp.
Objective This property is required. string
The objective for each stats.
featureStatsAnomaly This property is required. GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
The stats and anomalies generated at specific timestamp.
objective This property is required. String
The objective for each stats.
featureStatsAnomaly This property is required. GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
The stats and anomalies generated at specific timestamp.
objective This property is required. string
The objective for each stats.
feature_stats_anomaly This property is required. GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
The stats and anomalies generated at specific timestamp.
objective This property is required. str
The objective for each stats.
featureStatsAnomaly This property is required. Property Map
The stats and anomalies generated at specific timestamp.
objective This property is required. String
The objective for each stats.

GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse

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

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse

CategoricalThresholdConfig This property is required. 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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
The config for ImportFeatures Analysis Based Feature Monitoring.
NumericalThresholdConfig This property is required. 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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
The config for Snapshot Analysis Based Feature Monitoring.
CategoricalThresholdConfig This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
The config for ImportFeatures Analysis Based Feature Monitoring.
NumericalThresholdConfig This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
The config for Snapshot Analysis Based Feature Monitoring.
categoricalThresholdConfig This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
The config for ImportFeatures Analysis Based Feature Monitoring.
numericalThresholdConfig This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
The config for Snapshot Analysis Based Feature Monitoring.
categoricalThresholdConfig This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
The config for ImportFeatures Analysis Based Feature Monitoring.
numericalThresholdConfig This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
The config for Snapshot Analysis Based Feature Monitoring.
categorical_threshold_config This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
The config for ImportFeatures Analysis Based Feature Monitoring.
numerical_threshold_config This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
The config for Snapshot Analysis Based Feature Monitoring.
categoricalThresholdConfig This property is required. 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 This property is required. Property Map
The config for ImportFeatures Analysis Based Feature Monitoring.
numericalThresholdConfig This property is required. 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 This property is required. Property Map
The config for Snapshot Analysis Based Feature Monitoring.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse

Disabled This property is required. 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 This property is required. 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 This property is required. int
Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
StalenessDays This property is required. 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 This property is required. 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 This property is required. 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 This property is required. int
Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
StalenessDays This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Integer
Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
stalenessDays This property is required. 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 This property is required. 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 This property is required. 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 This property is required. number
Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
stalenessDays This property is required. 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 This property is required. 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 This property is required. 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 This property is required. int
Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
staleness_days This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Number
Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
stalenessDays This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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