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

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

google-native.aiplatform/v1beta1.FeatureGroupFeature

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

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

Creates a new Feature in a given FeatureGroup. Auto-naming is currently not supported for this resource.

Create FeatureGroupFeature Resource

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

Constructor syntax

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

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

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

Parameters

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

Constructor example

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

var google_nativeFeatureGroupFeatureResource = new GoogleNative.Aiplatform.V1Beta1.FeatureGroupFeature("google-nativeFeatureGroupFeatureResource", new()
{
    FeatureGroupId = "string",
    FeatureId = "string",
    Description = "string",
    DisableMonitoring = false,
    Etag = "string",
    Labels = 
    {
        { "string", "string" },
    },
    Location = "string",
    Name = "string",
    Project = "string",
    ValueType = GoogleNative.Aiplatform.V1Beta1.FeatureGroupFeatureValueType.ValueTypeUnspecified,
    VersionColumnName = "string",
});
Copy
example, err := aiplatformv1beta1.NewFeatureGroupFeature(ctx, "google-nativeFeatureGroupFeatureResource", &aiplatformv1beta1.FeatureGroupFeatureArgs{
	FeatureGroupId:    pulumi.String("string"),
	FeatureId:         pulumi.String("string"),
	Description:       pulumi.String("string"),
	DisableMonitoring: pulumi.Bool(false),
	Etag:              pulumi.String("string"),
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Location:          pulumi.String("string"),
	Name:              pulumi.String("string"),
	Project:           pulumi.String("string"),
	ValueType:         aiplatformv1beta1.FeatureGroupFeatureValueTypeValueTypeUnspecified,
	VersionColumnName: pulumi.String("string"),
})
Copy
var google_nativeFeatureGroupFeatureResource = new FeatureGroupFeature("google-nativeFeatureGroupFeatureResource", FeatureGroupFeatureArgs.builder()
    .featureGroupId("string")
    .featureId("string")
    .description("string")
    .disableMonitoring(false)
    .etag("string")
    .labels(Map.of("string", "string"))
    .location("string")
    .name("string")
    .project("string")
    .valueType("VALUE_TYPE_UNSPECIFIED")
    .versionColumnName("string")
    .build());
Copy
google_native_feature_group_feature_resource = google_native.aiplatform.v1beta1.FeatureGroupFeature("google-nativeFeatureGroupFeatureResource",
    feature_group_id="string",
    feature_id="string",
    description="string",
    disable_monitoring=False,
    etag="string",
    labels={
        "string": "string",
    },
    location="string",
    name="string",
    project="string",
    value_type=google_native.aiplatform.v1beta1.FeatureGroupFeatureValueType.VALUE_TYPE_UNSPECIFIED,
    version_column_name="string")
Copy
const google_nativeFeatureGroupFeatureResource = new google_native.aiplatform.v1beta1.FeatureGroupFeature("google-nativeFeatureGroupFeatureResource", {
    featureGroupId: "string",
    featureId: "string",
    description: "string",
    disableMonitoring: false,
    etag: "string",
    labels: {
        string: "string",
    },
    location: "string",
    name: "string",
    project: "string",
    valueType: google_native.aiplatform.v1beta1.FeatureGroupFeatureValueType.ValueTypeUnspecified,
    versionColumnName: "string",
});
Copy
type: google-native:aiplatform/v1beta1:FeatureGroupFeature
properties:
    description: string
    disableMonitoring: false
    etag: string
    featureGroupId: string
    featureId: string
    labels:
        string: string
    location: string
    name: string
    project: string
    valueType: VALUE_TYPE_UNSPECIFIED
    versionColumnName: string
Copy

FeatureGroupFeature Resource Properties

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

Inputs

In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

The FeatureGroupFeature resource accepts the following input properties:

FeatureGroupId
This property is required.
Changes to this property will trigger replacement.
string
FeatureId
This property is required.
Changes to this property will trigger replacement.
string
Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
Description string
Description of the Feature.
DisableMonitoring bool
Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
Etag string
Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
Labels Dictionary<string, string>
Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
Location Changes to this property will trigger replacement. string
MonitoringConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig
Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

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

Name string
Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
Project Changes to this property will trigger replacement. string
ValueType Pulumi.GoogleNative.Aiplatform.V1Beta1.FeatureGroupFeatureValueType
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.
FeatureGroupId
This property is required.
Changes to this property will trigger replacement.
string
FeatureId
This property is required.
Changes to this property will trigger replacement.
string
Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
Description string
Description of the Feature.
DisableMonitoring bool
Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
Etag string
Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
Labels map[string]string
Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
Location Changes to this property will trigger replacement. string
MonitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigArgs
Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

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

Name string
Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
Project Changes to this property will trigger replacement. string
ValueType FeatureGroupFeatureValueType
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.
featureGroupId
This property is required.
Changes to this property will trigger replacement.
String
featureId
This property is required.
Changes to this property will trigger replacement.
String
Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
description String
Description of the Feature.
disableMonitoring Boolean
Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
etag String
Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels Map<String,String>
Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
location Changes to this property will trigger replacement. String
monitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig
Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

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

name String
Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
project Changes to this property will trigger replacement. String
valueType FeatureGroupFeatureValueType
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.
featureGroupId
This property is required.
Changes to this property will trigger replacement.
string
featureId
This property is required.
Changes to this property will trigger replacement.
string
Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
description string
Description of the Feature.
disableMonitoring boolean
Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
etag string
Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels {[key: string]: string}
Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
location Changes to this property will trigger replacement. string
monitoringConfig GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig
Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

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

name string
Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
project Changes to this property will trigger replacement. string
valueType FeatureGroupFeatureValueType
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.
feature_group_id
This property is required.
Changes to this property will trigger replacement.
str
feature_id
This property is required.
Changes to this property will trigger replacement.
str
Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
description str
Description of the Feature.
disable_monitoring bool
Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
etag str
Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels Mapping[str, str]
Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
location Changes to this property will trigger replacement. str
monitoring_config GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigArgs
Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

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

name str
Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
project Changes to this property will trigger replacement. str
value_type FeatureGroupFeatureValueType
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.
featureGroupId
This property is required.
Changes to this property will trigger replacement.
String
featureId
This property is required.
Changes to this property will trigger replacement.
String
Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
description String
Description of the Feature.
disableMonitoring Boolean
Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
etag String
Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels Map<String>
Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
location Changes to this property will trigger replacement. String
monitoringConfig Property Map
Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.

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

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

Outputs

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

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

Supporting Types

FeatureGroupFeatureValueType
, FeatureGroupFeatureValueTypeArgs

ValueTypeUnspecified
VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
Bool
BOOLUsed for Feature that is a boolean.
BoolArray
BOOL_ARRAYUsed for Feature that is a list of boolean.
Double
DOUBLEUsed for Feature that is double.
DoubleArray
DOUBLE_ARRAYUsed for Feature that is a list of double.
Int64
INT64Used for Feature that is INT64.
Int64Array
INT64_ARRAYUsed for Feature that is a list of INT64.
String
STRINGUsed for Feature that is string.
StringArray
STRING_ARRAYUsed for Feature that is a list of String.
Bytes
BYTESUsed for Feature that is bytes.
FeatureGroupFeatureValueTypeValueTypeUnspecified
VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
FeatureGroupFeatureValueTypeBool
BOOLUsed for Feature that is a boolean.
FeatureGroupFeatureValueTypeBoolArray
BOOL_ARRAYUsed for Feature that is a list of boolean.
FeatureGroupFeatureValueTypeDouble
DOUBLEUsed for Feature that is double.
FeatureGroupFeatureValueTypeDoubleArray
DOUBLE_ARRAYUsed for Feature that is a list of double.
FeatureGroupFeatureValueTypeInt64
INT64Used for Feature that is INT64.
FeatureGroupFeatureValueTypeInt64Array
INT64_ARRAYUsed for Feature that is a list of INT64.
FeatureGroupFeatureValueTypeString
STRINGUsed for Feature that is string.
FeatureGroupFeatureValueTypeStringArray
STRING_ARRAYUsed for Feature that is a list of String.
FeatureGroupFeatureValueTypeBytes
BYTESUsed for Feature that is bytes.
ValueTypeUnspecified
VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
Bool
BOOLUsed for Feature that is a boolean.
BoolArray
BOOL_ARRAYUsed for Feature that is a list of boolean.
Double
DOUBLEUsed for Feature that is double.
DoubleArray
DOUBLE_ARRAYUsed for Feature that is a list of double.
Int64
INT64Used for Feature that is INT64.
Int64Array
INT64_ARRAYUsed for Feature that is a list of INT64.
String
STRINGUsed for Feature that is string.
StringArray
STRING_ARRAYUsed for Feature that is a list of String.
Bytes
BYTESUsed for Feature that is bytes.
ValueTypeUnspecified
VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
Bool
BOOLUsed for Feature that is a boolean.
BoolArray
BOOL_ARRAYUsed for Feature that is a list of boolean.
Double
DOUBLEUsed for Feature that is double.
DoubleArray
DOUBLE_ARRAYUsed for Feature that is a list of double.
Int64
INT64Used for Feature that is INT64.
Int64Array
INT64_ARRAYUsed for Feature that is a list of INT64.
String
STRINGUsed for Feature that is string.
StringArray
STRING_ARRAYUsed for Feature that is a list of String.
Bytes
BYTESUsed for Feature that is bytes.
VALUE_TYPE_UNSPECIFIED
VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
BOOL
BOOLUsed for Feature that is a boolean.
BOOL_ARRAY
BOOL_ARRAYUsed for Feature that is a list of boolean.
DOUBLE
DOUBLEUsed for Feature that is double.
DOUBLE_ARRAY
DOUBLE_ARRAYUsed for Feature that is a list of double.
INT64
INT64Used for Feature that is INT64.
INT64_ARRAY
INT64_ARRAYUsed for Feature that is a list of INT64.
STRING
STRINGUsed for Feature that is string.
STRING_ARRAY
STRING_ARRAYUsed for Feature that is a list of String.
BYTES
BYTESUsed for Feature that is bytes.
"VALUE_TYPE_UNSPECIFIED"
VALUE_TYPE_UNSPECIFIEDThe value type is unspecified.
"BOOL"
BOOLUsed for Feature that is a boolean.
"BOOL_ARRAY"
BOOL_ARRAYUsed for Feature that is a list of boolean.
"DOUBLE"
DOUBLEUsed for Feature that is double.
"DOUBLE_ARRAY"
DOUBLE_ARRAYUsed for Feature that is a list of double.
"INT64"
INT64Used for Feature that is INT64.
"INT64_ARRAY"
INT64_ARRAYUsed for Feature that is a list of INT64.
"STRING"
STRINGUsed for Feature that is string.
"STRING_ARRAY"
STRING_ARRAYUsed for Feature that is a list of String.
"BYTES"
BYTESUsed for Feature that is bytes.

GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponse
, GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomalyResponseArgs

FeatureStatsAnomaly 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
, GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponseArgs

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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigArgs

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

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisArgs

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

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaseline
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisAnomalyDetectionBaselineArgs

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

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponse
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisResponseArgs

AnomalyDetectionBaseline 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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisState
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysisStateArgs

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

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponse
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigResponseArgs

CategoricalThresholdConfig 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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisArgs

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

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponse
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysisResponseArgs

Disabled 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.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigArgs

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

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponse
, GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfigResponseArgs

Value 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
pulumi/pulumi-google-native