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

google-native.aiplatform/v1beta1.ModelDeploymentMonitoringJob

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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 ModelDeploymentMonitoringJob. It will run periodically on a configured interval. Auto-naming is currently not supported for this resource.

Create ModelDeploymentMonitoringJob Resource

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

Constructor syntax

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

@overload
def ModelDeploymentMonitoringJob(resource_name: str,
                                 opts: Optional[ResourceOptions] = None,
                                 logging_sampling_strategy: Optional[GoogleCloudAiplatformV1beta1SamplingStrategyArgs] = None,
                                 model_deployment_monitoring_objective_configs: Optional[Sequence[GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArgs]] = None,
                                 model_deployment_monitoring_schedule_config: Optional[GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigArgs] = None,
                                 display_name: Optional[str] = None,
                                 endpoint: Optional[str] = None,
                                 labels: Optional[Mapping[str, str]] = None,
                                 encryption_spec: Optional[GoogleCloudAiplatformV1beta1EncryptionSpecArgs] = None,
                                 location: Optional[str] = None,
                                 analysis_instance_schema_uri: Optional[str] = None,
                                 log_ttl: Optional[str] = None,
                                 enable_monitoring_pipeline_logs: Optional[bool] = None,
                                 model_monitoring_alert_config: Optional[GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs] = None,
                                 predict_instance_schema_uri: Optional[str] = None,
                                 project: Optional[str] = None,
                                 sample_predict_instance: Optional[Any] = None,
                                 stats_anomalies_base_directory: Optional[GoogleCloudAiplatformV1beta1GcsDestinationArgs] = None)
func NewModelDeploymentMonitoringJob(ctx *Context, name string, args ModelDeploymentMonitoringJobArgs, opts ...ResourceOption) (*ModelDeploymentMonitoringJob, error)
public ModelDeploymentMonitoringJob(string name, ModelDeploymentMonitoringJobArgs args, CustomResourceOptions? opts = null)
public ModelDeploymentMonitoringJob(String name, ModelDeploymentMonitoringJobArgs args)
public ModelDeploymentMonitoringJob(String name, ModelDeploymentMonitoringJobArgs args, CustomResourceOptions options)

type: google-native:aiplatform/v1beta1:ModelDeploymentMonitoringJob
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. ModelDeploymentMonitoringJobArgs
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. ModelDeploymentMonitoringJobArgs
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. ModelDeploymentMonitoringJobArgs
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. ModelDeploymentMonitoringJobArgs
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. ModelDeploymentMonitoringJobArgs
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_nativeModelDeploymentMonitoringJobResource = new GoogleNative.Aiplatform.V1Beta1.ModelDeploymentMonitoringJob("google-nativeModelDeploymentMonitoringJobResource", new()
{
    LoggingSamplingStrategy = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyArgs
    {
        RandomSampleConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs
        {
            SampleRate = 0,
        },
    },
    ModelDeploymentMonitoringObjectiveConfigs = new[]
    {
        new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArgs
        {
            DeployedModelId = "string",
            ObjectiveConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs
            {
                ExplanationConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs
                {
                    EnableFeatureAttributes = false,
                    ExplanationBaseline = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs
                    {
                        Bigquery = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs
                        {
                            OutputUri = "string",
                        },
                        Gcs = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationArgs
                        {
                            OutputUriPrefix = "string",
                        },
                        PredictionFormat = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat.PredictionFormatUnspecified,
                    },
                },
                PredictionDriftDetectionConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs
                {
                    AttributionScoreDriftThresholds = 
                    {
                        { "string", "string" },
                    },
                    DefaultDriftThreshold = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigArgs
                    {
                        Value = 0,
                    },
                    DriftThresholds = 
                    {
                        { "string", "string" },
                    },
                },
                TrainingDataset = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs
                {
                    BigquerySource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceArgs
                    {
                        InputUri = "string",
                    },
                    DataFormat = "string",
                    Dataset = "string",
                    GcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceArgs
                    {
                        Uris = new[]
                        {
                            "string",
                        },
                    },
                    LoggingSamplingStrategy = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyArgs
                    {
                        RandomSampleConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs
                        {
                            SampleRate = 0,
                        },
                    },
                    TargetField = "string",
                },
                TrainingPredictionSkewDetectionConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs
                {
                    AttributionScoreSkewThresholds = 
                    {
                        { "string", "string" },
                    },
                    DefaultSkewThreshold = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigArgs
                    {
                        Value = 0,
                    },
                    SkewThresholds = 
                    {
                        { "string", "string" },
                    },
                },
            },
        },
    },
    ModelDeploymentMonitoringScheduleConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigArgs
    {
        MonitorInterval = "string",
        MonitorWindow = "string",
    },
    DisplayName = "string",
    Endpoint = "string",
    Labels = 
    {
        { "string", "string" },
    },
    EncryptionSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    Location = "string",
    AnalysisInstanceSchemaUri = "string",
    LogTtl = "string",
    EnableMonitoringPipelineLogs = false,
    ModelMonitoringAlertConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs
    {
        EmailAlertConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs
        {
            UserEmails = new[]
            {
                "string",
            },
        },
        EnableLogging = false,
        NotificationChannels = new[]
        {
            "string",
        },
    },
    PredictInstanceSchemaUri = "string",
    Project = "string",
    SamplePredictInstance = "any",
    StatsAnomaliesBaseDirectory = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationArgs
    {
        OutputUriPrefix = "string",
    },
});
Copy
example, err := aiplatformv1beta1.NewModelDeploymentMonitoringJob(ctx, "google-nativeModelDeploymentMonitoringJobResource", &aiplatformv1beta1.ModelDeploymentMonitoringJobArgs{
	LoggingSamplingStrategy: &aiplatform.GoogleCloudAiplatformV1beta1SamplingStrategyArgs{
		RandomSampleConfig: &aiplatform.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs{
			SampleRate: pulumi.Float64(0),
		},
	},
	ModelDeploymentMonitoringObjectiveConfigs: aiplatform.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArray{
		&aiplatform.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArgs{
			DeployedModelId: pulumi.String("string"),
			ObjectiveConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs{
				ExplanationConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs{
					EnableFeatureAttributes: pulumi.Bool(false),
					ExplanationBaseline: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs{
						Bigquery: &aiplatform.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs{
							OutputUri: pulumi.String("string"),
						},
						Gcs: &aiplatform.GoogleCloudAiplatformV1beta1GcsDestinationArgs{
							OutputUriPrefix: pulumi.String("string"),
						},
						PredictionFormat: aiplatformv1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatPredictionFormatUnspecified,
					},
				},
				PredictionDriftDetectionConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs{
					AttributionScoreDriftThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
					DefaultDriftThreshold: &aiplatform.GoogleCloudAiplatformV1beta1ThresholdConfigArgs{
						Value: pulumi.Float64(0),
					},
					DriftThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
				},
				TrainingDataset: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs{
					BigquerySource: &aiplatform.GoogleCloudAiplatformV1beta1BigQuerySourceArgs{
						InputUri: pulumi.String("string"),
					},
					DataFormat: pulumi.String("string"),
					Dataset:    pulumi.String("string"),
					GcsSource: &aiplatform.GoogleCloudAiplatformV1beta1GcsSourceArgs{
						Uris: pulumi.StringArray{
							pulumi.String("string"),
						},
					},
					LoggingSamplingStrategy: &aiplatform.GoogleCloudAiplatformV1beta1SamplingStrategyArgs{
						RandomSampleConfig: &aiplatform.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs{
							SampleRate: pulumi.Float64(0),
						},
					},
					TargetField: pulumi.String("string"),
				},
				TrainingPredictionSkewDetectionConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs{
					AttributionScoreSkewThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
					DefaultSkewThreshold: &aiplatform.GoogleCloudAiplatformV1beta1ThresholdConfigArgs{
						Value: pulumi.Float64(0),
					},
					SkewThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
				},
			},
		},
	},
	ModelDeploymentMonitoringScheduleConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigArgs{
		MonitorInterval: pulumi.String("string"),
		MonitorWindow:   pulumi.String("string"),
	},
	DisplayName: pulumi.String("string"),
	Endpoint:    pulumi.String("string"),
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1beta1EncryptionSpecArgs{
		KmsKeyName: pulumi.String("string"),
	},
	Location:                     pulumi.String("string"),
	AnalysisInstanceSchemaUri:    pulumi.String("string"),
	LogTtl:                       pulumi.String("string"),
	EnableMonitoringPipelineLogs: pulumi.Bool(false),
	ModelMonitoringAlertConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs{
		EmailAlertConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs{
			UserEmails: pulumi.StringArray{
				pulumi.String("string"),
			},
		},
		EnableLogging: pulumi.Bool(false),
		NotificationChannels: pulumi.StringArray{
			pulumi.String("string"),
		},
	},
	PredictInstanceSchemaUri: pulumi.String("string"),
	Project:                  pulumi.String("string"),
	SamplePredictInstance:    pulumi.Any("any"),
	StatsAnomaliesBaseDirectory: &aiplatform.GoogleCloudAiplatformV1beta1GcsDestinationArgs{
		OutputUriPrefix: pulumi.String("string"),
	},
})
Copy
var google_nativeModelDeploymentMonitoringJobResource = new ModelDeploymentMonitoringJob("google-nativeModelDeploymentMonitoringJobResource", ModelDeploymentMonitoringJobArgs.builder()
    .loggingSamplingStrategy(GoogleCloudAiplatformV1beta1SamplingStrategyArgs.builder()
        .randomSampleConfig(GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs.builder()
            .sampleRate(0)
            .build())
        .build())
    .modelDeploymentMonitoringObjectiveConfigs(GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArgs.builder()
        .deployedModelId("string")
        .objectiveConfig(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs.builder()
            .explanationConfig(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs.builder()
                .enableFeatureAttributes(false)
                .explanationBaseline(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs.builder()
                    .bigquery(GoogleCloudAiplatformV1beta1BigQueryDestinationArgs.builder()
                        .outputUri("string")
                        .build())
                    .gcs(GoogleCloudAiplatformV1beta1GcsDestinationArgs.builder()
                        .outputUriPrefix("string")
                        .build())
                    .predictionFormat("PREDICTION_FORMAT_UNSPECIFIED")
                    .build())
                .build())
            .predictionDriftDetectionConfig(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs.builder()
                .attributionScoreDriftThresholds(Map.of("string", "string"))
                .defaultDriftThreshold(GoogleCloudAiplatformV1beta1ThresholdConfigArgs.builder()
                    .value(0)
                    .build())
                .driftThresholds(Map.of("string", "string"))
                .build())
            .trainingDataset(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs.builder()
                .bigquerySource(GoogleCloudAiplatformV1beta1BigQuerySourceArgs.builder()
                    .inputUri("string")
                    .build())
                .dataFormat("string")
                .dataset("string")
                .gcsSource(GoogleCloudAiplatformV1beta1GcsSourceArgs.builder()
                    .uris("string")
                    .build())
                .loggingSamplingStrategy(GoogleCloudAiplatformV1beta1SamplingStrategyArgs.builder()
                    .randomSampleConfig(GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs.builder()
                        .sampleRate(0)
                        .build())
                    .build())
                .targetField("string")
                .build())
            .trainingPredictionSkewDetectionConfig(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs.builder()
                .attributionScoreSkewThresholds(Map.of("string", "string"))
                .defaultSkewThreshold(GoogleCloudAiplatformV1beta1ThresholdConfigArgs.builder()
                    .value(0)
                    .build())
                .skewThresholds(Map.of("string", "string"))
                .build())
            .build())
        .build())
    .modelDeploymentMonitoringScheduleConfig(GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigArgs.builder()
        .monitorInterval("string")
        .monitorWindow("string")
        .build())
    .displayName("string")
    .endpoint("string")
    .labels(Map.of("string", "string"))
    .encryptionSpec(GoogleCloudAiplatformV1beta1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .location("string")
    .analysisInstanceSchemaUri("string")
    .logTtl("string")
    .enableMonitoringPipelineLogs(false)
    .modelMonitoringAlertConfig(GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs.builder()
        .emailAlertConfig(GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs.builder()
            .userEmails("string")
            .build())
        .enableLogging(false)
        .notificationChannels("string")
        .build())
    .predictInstanceSchemaUri("string")
    .project("string")
    .samplePredictInstance("any")
    .statsAnomaliesBaseDirectory(GoogleCloudAiplatformV1beta1GcsDestinationArgs.builder()
        .outputUriPrefix("string")
        .build())
    .build());
Copy
google_native_model_deployment_monitoring_job_resource = google_native.aiplatform.v1beta1.ModelDeploymentMonitoringJob("google-nativeModelDeploymentMonitoringJobResource",
    logging_sampling_strategy={
        "random_sample_config": {
            "sample_rate": 0,
        },
    },
    model_deployment_monitoring_objective_configs=[{
        "deployed_model_id": "string",
        "objective_config": {
            "explanation_config": {
                "enable_feature_attributes": False,
                "explanation_baseline": {
                    "bigquery": {
                        "output_uri": "string",
                    },
                    "gcs": {
                        "output_uri_prefix": "string",
                    },
                    "prediction_format": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat.PREDICTION_FORMAT_UNSPECIFIED,
                },
            },
            "prediction_drift_detection_config": {
                "attribution_score_drift_thresholds": {
                    "string": "string",
                },
                "default_drift_threshold": {
                    "value": 0,
                },
                "drift_thresholds": {
                    "string": "string",
                },
            },
            "training_dataset": {
                "bigquery_source": {
                    "input_uri": "string",
                },
                "data_format": "string",
                "dataset": "string",
                "gcs_source": {
                    "uris": ["string"],
                },
                "logging_sampling_strategy": {
                    "random_sample_config": {
                        "sample_rate": 0,
                    },
                },
                "target_field": "string",
            },
            "training_prediction_skew_detection_config": {
                "attribution_score_skew_thresholds": {
                    "string": "string",
                },
                "default_skew_threshold": {
                    "value": 0,
                },
                "skew_thresholds": {
                    "string": "string",
                },
            },
        },
    }],
    model_deployment_monitoring_schedule_config={
        "monitor_interval": "string",
        "monitor_window": "string",
    },
    display_name="string",
    endpoint="string",
    labels={
        "string": "string",
    },
    encryption_spec={
        "kms_key_name": "string",
    },
    location="string",
    analysis_instance_schema_uri="string",
    log_ttl="string",
    enable_monitoring_pipeline_logs=False,
    model_monitoring_alert_config={
        "email_alert_config": {
            "user_emails": ["string"],
        },
        "enable_logging": False,
        "notification_channels": ["string"],
    },
    predict_instance_schema_uri="string",
    project="string",
    sample_predict_instance="any",
    stats_anomalies_base_directory={
        "output_uri_prefix": "string",
    })
Copy
const google_nativeModelDeploymentMonitoringJobResource = new google_native.aiplatform.v1beta1.ModelDeploymentMonitoringJob("google-nativeModelDeploymentMonitoringJobResource", {
    loggingSamplingStrategy: {
        randomSampleConfig: {
            sampleRate: 0,
        },
    },
    modelDeploymentMonitoringObjectiveConfigs: [{
        deployedModelId: "string",
        objectiveConfig: {
            explanationConfig: {
                enableFeatureAttributes: false,
                explanationBaseline: {
                    bigquery: {
                        outputUri: "string",
                    },
                    gcs: {
                        outputUriPrefix: "string",
                    },
                    predictionFormat: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat.PredictionFormatUnspecified,
                },
            },
            predictionDriftDetectionConfig: {
                attributionScoreDriftThresholds: {
                    string: "string",
                },
                defaultDriftThreshold: {
                    value: 0,
                },
                driftThresholds: {
                    string: "string",
                },
            },
            trainingDataset: {
                bigquerySource: {
                    inputUri: "string",
                },
                dataFormat: "string",
                dataset: "string",
                gcsSource: {
                    uris: ["string"],
                },
                loggingSamplingStrategy: {
                    randomSampleConfig: {
                        sampleRate: 0,
                    },
                },
                targetField: "string",
            },
            trainingPredictionSkewDetectionConfig: {
                attributionScoreSkewThresholds: {
                    string: "string",
                },
                defaultSkewThreshold: {
                    value: 0,
                },
                skewThresholds: {
                    string: "string",
                },
            },
        },
    }],
    modelDeploymentMonitoringScheduleConfig: {
        monitorInterval: "string",
        monitorWindow: "string",
    },
    displayName: "string",
    endpoint: "string",
    labels: {
        string: "string",
    },
    encryptionSpec: {
        kmsKeyName: "string",
    },
    location: "string",
    analysisInstanceSchemaUri: "string",
    logTtl: "string",
    enableMonitoringPipelineLogs: false,
    modelMonitoringAlertConfig: {
        emailAlertConfig: {
            userEmails: ["string"],
        },
        enableLogging: false,
        notificationChannels: ["string"],
    },
    predictInstanceSchemaUri: "string",
    project: "string",
    samplePredictInstance: "any",
    statsAnomaliesBaseDirectory: {
        outputUriPrefix: "string",
    },
});
Copy
type: google-native:aiplatform/v1beta1:ModelDeploymentMonitoringJob
properties:
    analysisInstanceSchemaUri: string
    displayName: string
    enableMonitoringPipelineLogs: false
    encryptionSpec:
        kmsKeyName: string
    endpoint: string
    labels:
        string: string
    location: string
    logTtl: string
    loggingSamplingStrategy:
        randomSampleConfig:
            sampleRate: 0
    modelDeploymentMonitoringObjectiveConfigs:
        - deployedModelId: string
          objectiveConfig:
            explanationConfig:
                enableFeatureAttributes: false
                explanationBaseline:
                    bigquery:
                        outputUri: string
                    gcs:
                        outputUriPrefix: string
                    predictionFormat: PREDICTION_FORMAT_UNSPECIFIED
            predictionDriftDetectionConfig:
                attributionScoreDriftThresholds:
                    string: string
                defaultDriftThreshold:
                    value: 0
                driftThresholds:
                    string: string
            trainingDataset:
                bigquerySource:
                    inputUri: string
                dataFormat: string
                dataset: string
                gcsSource:
                    uris:
                        - string
                loggingSamplingStrategy:
                    randomSampleConfig:
                        sampleRate: 0
                targetField: string
            trainingPredictionSkewDetectionConfig:
                attributionScoreSkewThresholds:
                    string: string
                defaultSkewThreshold:
                    value: 0
                skewThresholds:
                    string: string
    modelDeploymentMonitoringScheduleConfig:
        monitorInterval: string
        monitorWindow: string
    modelMonitoringAlertConfig:
        emailAlertConfig:
            userEmails:
                - string
        enableLogging: false
        notificationChannels:
            - string
    predictInstanceSchemaUri: string
    project: string
    samplePredictInstance: any
    statsAnomaliesBaseDirectory:
        outputUriPrefix: string
Copy

ModelDeploymentMonitoringJob 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 ModelDeploymentMonitoringJob resource accepts the following input properties:

DisplayName This property is required. string
The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
Endpoint This property is required. string
Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
LoggingSamplingStrategy This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategy
Sample Strategy for logging.
ModelDeploymentMonitoringObjectiveConfigs This property is required. List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfig>
The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
ModelDeploymentMonitoringScheduleConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfig
Schedule config for running the monitoring job.
AnalysisInstanceSchemaUri string
YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
EnableMonitoringPipelineLogs bool
If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
Labels Dictionary<string, string>
The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. 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 and examples of labels.
Location Changes to this property will trigger replacement. string
LogTtl string
The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
ModelMonitoringAlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
Alert config for model monitoring.
PredictInstanceSchemaUri string
YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
Project Changes to this property will trigger replacement. string
SamplePredictInstance object
Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
StatsAnomaliesBaseDirectory Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestination
Stats anomalies base folder path.
DisplayName This property is required. string
The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
Endpoint This property is required. string
Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
LoggingSamplingStrategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyArgs
Sample Strategy for logging.
ModelDeploymentMonitoringObjectiveConfigs This property is required. []GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArgs
The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
ModelDeploymentMonitoringScheduleConfig This property is required. GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigArgs
Schedule config for running the monitoring job.
AnalysisInstanceSchemaUri string
YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
EnableMonitoringPipelineLogs bool
If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecArgs
Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
Labels map[string]string
The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. 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 and examples of labels.
Location Changes to this property will trigger replacement. string
LogTtl string
The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
ModelMonitoringAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs
Alert config for model monitoring.
PredictInstanceSchemaUri string
YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
Project Changes to this property will trigger replacement. string
SamplePredictInstance interface{}
Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
StatsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestinationArgs
Stats anomalies base folder path.
displayName This property is required. String
The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
endpoint This property is required. String
Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
loggingSamplingStrategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategy
Sample Strategy for logging.
modelDeploymentMonitoringObjectiveConfigs This property is required. List<GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfig>
The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
modelDeploymentMonitoringScheduleConfig This property is required. GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfig
Schedule config for running the monitoring job.
analysisInstanceSchemaUri String
YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
enableMonitoringPipelineLogs Boolean
If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
labels Map<String,String>
The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. 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 and examples of labels.
location Changes to this property will trigger replacement. String
logTtl String
The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
modelMonitoringAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
Alert config for model monitoring.
predictInstanceSchemaUri String
YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
project Changes to this property will trigger replacement. String
samplePredictInstance Object
Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestination
Stats anomalies base folder path.
displayName This property is required. string
The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
endpoint This property is required. string
Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
loggingSamplingStrategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategy
Sample Strategy for logging.
modelDeploymentMonitoringObjectiveConfigs This property is required. GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfig[]
The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
modelDeploymentMonitoringScheduleConfig This property is required. GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfig
Schedule config for running the monitoring job.
analysisInstanceSchemaUri string
YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
enableMonitoringPipelineLogs boolean
If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
labels {[key: string]: string}
The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. 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 and examples of labels.
location Changes to this property will trigger replacement. string
logTtl string
The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
modelMonitoringAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
Alert config for model monitoring.
predictInstanceSchemaUri string
YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
project Changes to this property will trigger replacement. string
samplePredictInstance any
Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestination
Stats anomalies base folder path.
display_name This property is required. str
The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
endpoint This property is required. str
Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
logging_sampling_strategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyArgs
Sample Strategy for logging.
model_deployment_monitoring_objective_configs This property is required. Sequence[GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArgs]
The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
model_deployment_monitoring_schedule_config This property is required. GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigArgs
Schedule config for running the monitoring job.
analysis_instance_schema_uri str
YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
enable_monitoring_pipeline_logs bool
If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecArgs
Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
labels Mapping[str, str]
The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. 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 and examples of labels.
location Changes to this property will trigger replacement. str
log_ttl str
The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
model_monitoring_alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs
Alert config for model monitoring.
predict_instance_schema_uri str
YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
project Changes to this property will trigger replacement. str
sample_predict_instance Any
Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
stats_anomalies_base_directory GoogleCloudAiplatformV1beta1GcsDestinationArgs
Stats anomalies base folder path.
displayName This property is required. String
The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
endpoint This property is required. String
Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
loggingSamplingStrategy This property is required. Property Map
Sample Strategy for logging.
modelDeploymentMonitoringObjectiveConfigs This property is required. List<Property Map>
The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
modelDeploymentMonitoringScheduleConfig This property is required. Property Map
Schedule config for running the monitoring job.
analysisInstanceSchemaUri String
YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
enableMonitoringPipelineLogs Boolean
If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
encryptionSpec Property Map
Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
labels Map<String>
The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. 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 and examples of labels.
location Changes to this property will trigger replacement. String
logTtl String
The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
modelMonitoringAlertConfig Property Map
Alert config for model monitoring.
predictInstanceSchemaUri String
YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
project Changes to this property will trigger replacement. String
samplePredictInstance Any
Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
statsAnomaliesBaseDirectory Property Map
Stats anomalies base folder path.

Outputs

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

BigqueryTables List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse>
The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
CreateTime string
Timestamp when this ModelDeploymentMonitoringJob was created.
Error Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
Id string
The provider-assigned unique ID for this managed resource.
LatestMonitoringPipelineMetadata Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
Latest triggered monitoring pipeline metadata.
Name string
Resource name of a ModelDeploymentMonitoringJob.
NextScheduleTime string
Timestamp when this monitoring pipeline will be scheduled to run for the next round.
ScheduleState string
Schedule state when the monitoring job is in Running state.
State string
The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
UpdateTime string
Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
BigqueryTables []GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse
The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
CreateTime string
Timestamp when this ModelDeploymentMonitoringJob was created.
Error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
Id string
The provider-assigned unique ID for this managed resource.
LatestMonitoringPipelineMetadata GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
Latest triggered monitoring pipeline metadata.
Name string
Resource name of a ModelDeploymentMonitoringJob.
NextScheduleTime string
Timestamp when this monitoring pipeline will be scheduled to run for the next round.
ScheduleState string
Schedule state when the monitoring job is in Running state.
State string
The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
UpdateTime string
Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
bigqueryTables List<GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse>
The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
createTime String
Timestamp when this ModelDeploymentMonitoringJob was created.
error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id String
The provider-assigned unique ID for this managed resource.
latestMonitoringPipelineMetadata GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
Latest triggered monitoring pipeline metadata.
name String
Resource name of a ModelDeploymentMonitoringJob.
nextScheduleTime String
Timestamp when this monitoring pipeline will be scheduled to run for the next round.
scheduleState String
Schedule state when the monitoring job is in Running state.
state String
The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
updateTime String
Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
bigqueryTables GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse[]
The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
createTime string
Timestamp when this ModelDeploymentMonitoringJob was created.
error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id string
The provider-assigned unique ID for this managed resource.
latestMonitoringPipelineMetadata GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
Latest triggered monitoring pipeline metadata.
name string
Resource name of a ModelDeploymentMonitoringJob.
nextScheduleTime string
Timestamp when this monitoring pipeline will be scheduled to run for the next round.
scheduleState string
Schedule state when the monitoring job is in Running state.
state string
The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
updateTime string
Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
bigquery_tables Sequence[GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse]
The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
create_time str
Timestamp when this ModelDeploymentMonitoringJob was created.
error GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id str
The provider-assigned unique ID for this managed resource.
latest_monitoring_pipeline_metadata GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
Latest triggered monitoring pipeline metadata.
name str
Resource name of a ModelDeploymentMonitoringJob.
next_schedule_time str
Timestamp when this monitoring pipeline will be scheduled to run for the next round.
schedule_state str
Schedule state when the monitoring job is in Running state.
state str
The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
update_time str
Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
bigqueryTables List<Property Map>
The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
createTime String
Timestamp when this ModelDeploymentMonitoringJob was created.
error Property Map
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
id String
The provider-assigned unique ID for this managed resource.
latestMonitoringPipelineMetadata Property Map
Latest triggered monitoring pipeline metadata.
name String
Resource name of a ModelDeploymentMonitoringJob.
nextScheduleTime String
Timestamp when this monitoring pipeline will be scheduled to run for the next round.
scheduleState String
Schedule state when the monitoring job is in Running state.
state String
The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
updateTime String
Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

Supporting Types

GoogleCloudAiplatformV1beta1BigQueryDestination
, GoogleCloudAiplatformV1beta1BigQueryDestinationArgs

OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
output_uri This property is required. str
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
, GoogleCloudAiplatformV1beta1BigQueryDestinationResponseArgs

OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
OutputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. string
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
output_uri This property is required. str
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
outputUri This property is required. String
BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1beta1BigQuerySource
, GoogleCloudAiplatformV1beta1BigQuerySourceArgs

InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
input_uri This property is required. str
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1beta1BigQuerySourceResponse
, GoogleCloudAiplatformV1beta1BigQuerySourceResponseArgs

InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
InputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. string
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
input_uri This property is required. str
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
inputUri This property is required. String
BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1beta1EncryptionSpec
, GoogleCloudAiplatformV1beta1EncryptionSpecArgs

KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kms_key_name This property is required. str
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

GoogleCloudAiplatformV1beta1EncryptionSpecResponse
, GoogleCloudAiplatformV1beta1EncryptionSpecResponseArgs

KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
KmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. string
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kms_key_name This property is required. str
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
kmsKeyName This property is required. String
The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

GoogleCloudAiplatformV1beta1GcsDestination
, GoogleCloudAiplatformV1beta1GcsDestinationArgs

OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
output_uri_prefix This property is required. str
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

GoogleCloudAiplatformV1beta1GcsDestinationResponse
, GoogleCloudAiplatformV1beta1GcsDestinationResponseArgs

OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
OutputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. string
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
output_uri_prefix This property is required. str
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
outputUriPrefix This property is required. String
Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

GoogleCloudAiplatformV1beta1GcsSource
, GoogleCloudAiplatformV1beta1GcsSourceArgs

Uris This property is required. List<string>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
Uris This property is required. []string
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. string[]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. Sequence[str]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

GoogleCloudAiplatformV1beta1GcsSourceResponse
, GoogleCloudAiplatformV1beta1GcsSourceResponseArgs

Uris This property is required. List<string>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
Uris This property is required. []string
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. string[]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. Sequence[str]
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
uris This property is required. List<String>
Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse
, GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponseArgs

BigqueryTablePath This property is required. string
The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://.model_deployment_monitoring_._
LogSource This property is required. string
The source of log.
LogType This property is required. string
The type of log.
BigqueryTablePath This property is required. string
The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://.model_deployment_monitoring_._
LogSource This property is required. string
The source of log.
LogType This property is required. string
The type of log.
bigqueryTablePath This property is required. String
The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://.model_deployment_monitoring_._
logSource This property is required. String
The source of log.
logType This property is required. String
The type of log.
bigqueryTablePath This property is required. string
The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://.model_deployment_monitoring_._
logSource This property is required. string
The source of log.
logType This property is required. string
The type of log.
bigquery_table_path This property is required. str
The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://.model_deployment_monitoring_._
log_source This property is required. str
The source of log.
log_type This property is required. str
The type of log.
bigqueryTablePath This property is required. String
The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://.model_deployment_monitoring_._
logSource This property is required. String
The source of log.
logType This property is required. String
The type of log.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
, GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponseArgs

RunTime This property is required. string
The time that most recent monitoring pipelines that is related to this run.
Status This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleRpcStatusResponse
The status of the most recent monitoring pipeline.
RunTime This property is required. string
The time that most recent monitoring pipelines that is related to this run.
Status This property is required. GoogleRpcStatusResponse
The status of the most recent monitoring pipeline.
runTime This property is required. String
The time that most recent monitoring pipelines that is related to this run.
status This property is required. GoogleRpcStatusResponse
The status of the most recent monitoring pipeline.
runTime This property is required. string
The time that most recent monitoring pipelines that is related to this run.
status This property is required. GoogleRpcStatusResponse
The status of the most recent monitoring pipeline.
run_time This property is required. str
The time that most recent monitoring pipelines that is related to this run.
status This property is required. GoogleRpcStatusResponse
The status of the most recent monitoring pipeline.
runTime This property is required. String
The time that most recent monitoring pipelines that is related to this run.
status This property is required. Property Map
The status of the most recent monitoring pipeline.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfig
, GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigArgs

DeployedModelId string
The DeployedModel ID of the objective config.
ObjectiveConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig
The objective config of for the modelmonitoring job of this deployed model.
DeployedModelId string
The DeployedModel ID of the objective config.
ObjectiveConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig
The objective config of for the modelmonitoring job of this deployed model.
deployedModelId String
The DeployedModel ID of the objective config.
objectiveConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig
The objective config of for the modelmonitoring job of this deployed model.
deployedModelId string
The DeployedModel ID of the objective config.
objectiveConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig
The objective config of for the modelmonitoring job of this deployed model.
deployed_model_id str
The DeployedModel ID of the objective config.
objective_config GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig
The objective config of for the modelmonitoring job of this deployed model.
deployedModelId String
The DeployedModel ID of the objective config.
objectiveConfig Property Map
The objective config of for the modelmonitoring job of this deployed model.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse
, GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponseArgs

DeployedModelId This property is required. string
The DeployedModel ID of the objective config.
ObjectiveConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse
The objective config of for the modelmonitoring job of this deployed model.
DeployedModelId This property is required. string
The DeployedModel ID of the objective config.
ObjectiveConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse
The objective config of for the modelmonitoring job of this deployed model.
deployedModelId This property is required. String
The DeployedModel ID of the objective config.
objectiveConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse
The objective config of for the modelmonitoring job of this deployed model.
deployedModelId This property is required. string
The DeployedModel ID of the objective config.
objectiveConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse
The objective config of for the modelmonitoring job of this deployed model.
deployed_model_id This property is required. str
The DeployedModel ID of the objective config.
objective_config This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse
The objective config of for the modelmonitoring job of this deployed model.
deployedModelId This property is required. String
The DeployedModel ID of the objective config.
objectiveConfig This property is required. Property Map
The objective config of for the modelmonitoring job of this deployed model.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfig
, GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigArgs

MonitorInterval This property is required. string
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
MonitorWindow string
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
MonitorInterval This property is required. string
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
MonitorWindow string
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitorInterval This property is required. String
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitorWindow String
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitorInterval This property is required. string
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitorWindow string
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitor_interval This property is required. str
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitor_window str
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitorInterval This property is required. String
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitorWindow String
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse
, GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponseArgs

MonitorInterval This property is required. string
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
MonitorWindow This property is required. string
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
MonitorInterval This property is required. string
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
MonitorWindow This property is required. string
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitorInterval This property is required. String
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitorWindow This property is required. String
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitorInterval This property is required. string
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitorWindow This property is required. string
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitor_interval This property is required. str
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitor_window This property is required. str
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.
monitorInterval This property is required. String
The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitorWindow This property is required. String
The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs

EmailAlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
Email alert config.
EnableLogging bool
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
NotificationChannels List<string>
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
EmailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
Email alert config.
EnableLogging bool
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
NotificationChannels []string
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
Email alert config.
enableLogging Boolean
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels List<String>
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
Email alert config.
enableLogging boolean
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels string[]
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
email_alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
Email alert config.
enable_logging bool
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notification_channels Sequence[str]
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
emailAlertConfig Property Map
Email alert config.
enableLogging Boolean
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels List<String>
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs

UserEmails List<string>
The email addresses to send the alert.
UserEmails []string
The email addresses to send the alert.
userEmails List<String>
The email addresses to send the alert.
userEmails string[]
The email addresses to send the alert.
user_emails Sequence[str]
The email addresses to send the alert.
userEmails List<String>
The email addresses to send the alert.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponseArgs

UserEmails This property is required. List<string>
The email addresses to send the alert.
UserEmails This property is required. []string
The email addresses to send the alert.
userEmails This property is required. List<String>
The email addresses to send the alert.
userEmails This property is required. string[]
The email addresses to send the alert.
user_emails This property is required. Sequence[str]
The email addresses to send the alert.
userEmails This property is required. List<String>
The email addresses to send the alert.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponseArgs

EmailAlertConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
Email alert config.
EnableLogging This property is required. bool
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
NotificationChannels This property is required. List<string>
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
EmailAlertConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
Email alert config.
EnableLogging This property is required. bool
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
NotificationChannels This property is required. []string
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
emailAlertConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
Email alert config.
enableLogging This property is required. Boolean
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels This property is required. List<String>
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
emailAlertConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
Email alert config.
enableLogging This property is required. boolean
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels This property is required. string[]
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
email_alert_config This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
Email alert config.
enable_logging This property is required. bool
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notification_channels This property is required. Sequence[str]
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
emailAlertConfig This property is required. Property Map
Email alert config.
enableLogging This property is required. Boolean
Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels This property is required. List<String>
Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs

ExplanationConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfig
The config for integrating with Vertex Explainable AI.
PredictionDriftDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig
The config for drift of prediction data.
TrainingDataset Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDataset
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
TrainingPredictionSkewDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig
The config for skew between training data and prediction data.
explanationConfig Property Map
The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig Property Map
The config for drift of prediction data.
trainingDataset Property Map
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig Property Map
The config for skew between training data and prediction data.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfig
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs

EnableFeatureAttributes bool
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
ExplanationBaseline Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
Predictions generated by the BatchPredictionJob using baseline dataset.
EnableFeatureAttributes bool
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
ExplanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
Predictions generated by the BatchPredictionJob using baseline dataset.
enableFeatureAttributes Boolean
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
Predictions generated by the BatchPredictionJob using baseline dataset.
enableFeatureAttributes boolean
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
Predictions generated by the BatchPredictionJob using baseline dataset.
enable_feature_attributes bool
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanation_baseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
Predictions generated by the BatchPredictionJob using baseline dataset.
enableFeatureAttributes Boolean
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline Property Map
Predictions generated by the BatchPredictionJob using baseline dataset.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs

Bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
BigQuery location for BatchExplain output.
Gcs GoogleCloudAiplatformV1beta1GcsDestination
Cloud Storage location for BatchExplain output.
PredictionFormat GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
The storage format of the predictions generated BatchPrediction job.
bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestination
Cloud Storage location for BatchExplain output.
predictionFormat GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
The storage format of the predictions generated BatchPrediction job.
bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestination
Cloud Storage location for BatchExplain output.
predictionFormat GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
The storage format of the predictions generated BatchPrediction job.
bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestination
Cloud Storage location for BatchExplain output.
prediction_format GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
The storage format of the predictions generated BatchPrediction job.
bigquery Property Map
BigQuery location for BatchExplain output.
gcs Property Map
Cloud Storage location for BatchExplain output.
predictionFormat "PREDICTION_FORMAT_UNSPECIFIED" | "JSONL" | "BIGQUERY"
The storage format of the predictions generated BatchPrediction job.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatArgs

PredictionFormatUnspecified
PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
Jsonl
JSONLPredictions are in JSONL files.
Bigquery
BIGQUERYPredictions are in BigQuery.
GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatPredictionFormatUnspecified
PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatJsonl
JSONLPredictions are in JSONL files.
GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatBigquery
BIGQUERYPredictions are in BigQuery.
PredictionFormatUnspecified
PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
Jsonl
JSONLPredictions are in JSONL files.
Bigquery
BIGQUERYPredictions are in BigQuery.
PredictionFormatUnspecified
PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
Jsonl
JSONLPredictions are in JSONL files.
Bigquery
BIGQUERYPredictions are in BigQuery.
PREDICTION_FORMAT_UNSPECIFIED
PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
JSONL
JSONLPredictions are in JSONL files.
BIGQUERY
BIGQUERYPredictions are in BigQuery.
"PREDICTION_FORMAT_UNSPECIFIED"
PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
"JSONL"
JSONLPredictions are in JSONL files.
"BIGQUERY"
BIGQUERYPredictions are in BigQuery.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponseArgs

Bigquery This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
BigQuery location for BatchExplain output.
Gcs This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse
Cloud Storage location for BatchExplain output.
PredictionFormat This property is required. string
The storage format of the predictions generated BatchPrediction job.
Bigquery This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
BigQuery location for BatchExplain output.
Gcs This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
Cloud Storage location for BatchExplain output.
PredictionFormat This property is required. string
The storage format of the predictions generated BatchPrediction job.
bigquery This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
BigQuery location for BatchExplain output.
gcs This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
Cloud Storage location for BatchExplain output.
predictionFormat This property is required. String
The storage format of the predictions generated BatchPrediction job.
bigquery This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
BigQuery location for BatchExplain output.
gcs This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
Cloud Storage location for BatchExplain output.
predictionFormat This property is required. string
The storage format of the predictions generated BatchPrediction job.
bigquery This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
BigQuery location for BatchExplain output.
gcs This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
Cloud Storage location for BatchExplain output.
prediction_format This property is required. str
The storage format of the predictions generated BatchPrediction job.
bigquery This property is required. Property Map
BigQuery location for BatchExplain output.
gcs This property is required. Property Map
Cloud Storage location for BatchExplain output.
predictionFormat This property is required. String
The storage format of the predictions generated BatchPrediction job.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponseArgs

EnableFeatureAttributes This property is required. bool
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
ExplanationBaseline This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
Predictions generated by the BatchPredictionJob using baseline dataset.
EnableFeatureAttributes This property is required. bool
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
ExplanationBaseline This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
Predictions generated by the BatchPredictionJob using baseline dataset.
enableFeatureAttributes This property is required. Boolean
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
Predictions generated by the BatchPredictionJob using baseline dataset.
enableFeatureAttributes This property is required. boolean
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
Predictions generated by the BatchPredictionJob using baseline dataset.
enable_feature_attributes This property is required. bool
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanation_baseline This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
Predictions generated by the BatchPredictionJob using baseline dataset.
enableFeatureAttributes This property is required. Boolean
If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline This property is required. Property Map
Predictions generated by the BatchPredictionJob using baseline dataset.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs

AttributionScoreDriftThresholds Dictionary<string, string>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
DefaultDriftThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfig
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
DriftThresholds Dictionary<string, string>
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
AttributionScoreDriftThresholds map[string]string
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
DefaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
DriftThresholds map[string]string
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attributionScoreDriftThresholds Map<String,String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds Map<String,String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attributionScoreDriftThresholds {[key: string]: string}
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds {[key: string]: string}
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attribution_score_drift_thresholds Mapping[str, str]
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
default_drift_threshold GoogleCloudAiplatformV1beta1ThresholdConfig
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
drift_thresholds Mapping[str, str]
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attributionScoreDriftThresholds Map<String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold Property Map
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds Map<String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponseArgs

AttributionScoreDriftThresholds This property is required. Dictionary<string, string>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
DefaultDriftThreshold This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
DriftThresholds This property is required. Dictionary<string, string>
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
AttributionScoreDriftThresholds This property is required. map[string]string
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
DefaultDriftThreshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
DriftThresholds This property is required. map[string]string
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attributionScoreDriftThresholds This property is required. Map<String,String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds This property is required. Map<String,String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attributionScoreDriftThresholds This property is required. {[key: string]: string}
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds This property is required. {[key: string]: string}
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attribution_score_drift_thresholds This property is required. Mapping[str, str]
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
default_drift_threshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
drift_thresholds This property is required. Mapping[str, str]
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
attributionScoreDriftThresholds This property is required. Map<String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold This property is required. Property Map
Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds This property is required. Map<String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponseArgs

ExplanationConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse
The config for integrating with Vertex Explainable AI.
PredictionDriftDetectionConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
The config for drift of prediction data.
TrainingDataset This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
TrainingPredictionSkewDetectionConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
The config for skew between training data and prediction data.
ExplanationConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse
The config for integrating with Vertex Explainable AI.
PredictionDriftDetectionConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
The config for drift of prediction data.
TrainingDataset This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
TrainingPredictionSkewDetectionConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
The config for skew between training data and prediction data.
explanationConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse
The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
The config for drift of prediction data.
trainingDataset This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
The config for skew between training data and prediction data.
explanationConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse
The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
The config for drift of prediction data.
trainingDataset This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
The config for skew between training data and prediction data.
explanation_config This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse
The config for integrating with Vertex Explainable AI.
prediction_drift_detection_config This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
The config for drift of prediction data.
training_dataset This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
training_prediction_skew_detection_config This property is required. GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
The config for skew between training data and prediction data.
explanationConfig This property is required. Property Map
The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig This property is required. Property Map
The config for drift of prediction data.
trainingDataset This property is required. Property Map
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig This property is required. Property Map
The config for skew between training data and prediction data.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDataset
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs

BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySource
The BigQuery table of the unmanaged Dataset used to train this Model.
DataFormat string
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
Dataset string
The resource name of the Dataset used to train this Model.
GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSource
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategy
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
TargetField string
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
BigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
The BigQuery table of the unmanaged Dataset used to train this Model.
DataFormat string
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
Dataset string
The resource name of the Dataset used to train this Model.
GcsSource GoogleCloudAiplatformV1beta1GcsSource
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategy
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
TargetField string
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat String
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset String
The resource name of the Dataset used to train this Model.
gcsSource GoogleCloudAiplatformV1beta1GcsSource
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategy
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField String
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat string
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset string
The resource name of the Dataset used to train this Model.
gcsSource GoogleCloudAiplatformV1beta1GcsSource
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategy
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField string
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquery_source GoogleCloudAiplatformV1beta1BigQuerySource
The BigQuery table of the unmanaged Dataset used to train this Model.
data_format str
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset str
The resource name of the Dataset used to train this Model.
gcs_source GoogleCloudAiplatformV1beta1GcsSource
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
logging_sampling_strategy GoogleCloudAiplatformV1beta1SamplingStrategy
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
target_field str
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquerySource Property Map
The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat String
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset String
The resource name of the Dataset used to train this Model.
gcsSource Property Map
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy Property Map
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField String
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponseArgs

BigquerySource This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceResponse
The BigQuery table of the unmanaged Dataset used to train this Model.
DataFormat This property is required. string
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
Dataset This property is required. string
The resource name of the Dataset used to train this Model.
GcsSource This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyResponse
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
TargetField This property is required. string
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
BigquerySource This property is required. GoogleCloudAiplatformV1beta1BigQuerySourceResponse
The BigQuery table of the unmanaged Dataset used to train this Model.
DataFormat This property is required. string
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
Dataset This property is required. string
The resource name of the Dataset used to train this Model.
GcsSource This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyResponse
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
TargetField This property is required. string
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquerySource This property is required. GoogleCloudAiplatformV1beta1BigQuerySourceResponse
The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat This property is required. String
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset This property is required. String
The resource name of the Dataset used to train this Model.
gcsSource This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyResponse
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField This property is required. String
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquerySource This property is required. GoogleCloudAiplatformV1beta1BigQuerySourceResponse
The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat This property is required. string
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset This property is required. string
The resource name of the Dataset used to train this Model.
gcsSource This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyResponse
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField This property is required. string
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquery_source This property is required. GoogleCloudAiplatformV1beta1BigQuerySourceResponse
The BigQuery table of the unmanaged Dataset used to train this Model.
data_format This property is required. str
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset This property is required. str
The resource name of the Dataset used to train this Model.
gcs_source This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
logging_sampling_strategy This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyResponse
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
target_field This property is required. str
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
bigquerySource This property is required. Property Map
The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat This property is required. String
Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset This property is required. String
The resource name of the Dataset used to train this Model.
gcsSource This property is required. Property Map
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy This property is required. Property Map
Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField This property is required. String
The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs

AttributionScoreSkewThresholds Dictionary<string, string>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
DefaultSkewThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfig
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
SkewThresholds Dictionary<string, string>
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
AttributionScoreSkewThresholds map[string]string
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
DefaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
SkewThresholds map[string]string
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attributionScoreSkewThresholds Map<String,String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds Map<String,String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attributionScoreSkewThresholds {[key: string]: string}
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds {[key: string]: string}
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attribution_score_skew_thresholds Mapping[str, str]
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
default_skew_threshold GoogleCloudAiplatformV1beta1ThresholdConfig
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skew_thresholds Mapping[str, str]
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attributionScoreSkewThresholds Map<String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold Property Map
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds Map<String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponseArgs

AttributionScoreSkewThresholds This property is required. Dictionary<string, string>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
DefaultSkewThreshold This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
SkewThresholds This property is required. Dictionary<string, string>
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
AttributionScoreSkewThresholds This property is required. map[string]string
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
DefaultSkewThreshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
SkewThresholds This property is required. map[string]string
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attributionScoreSkewThresholds This property is required. Map<String,String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds This property is required. Map<String,String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attributionScoreSkewThresholds This property is required. {[key: string]: string}
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds This property is required. {[key: string]: string}
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attribution_score_skew_thresholds This property is required. Mapping[str, str]
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
default_skew_threshold This property is required. GoogleCloudAiplatformV1beta1ThresholdConfigResponse
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skew_thresholds This property is required. Mapping[str, str]
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
attributionScoreSkewThresholds This property is required. Map<String>
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold This property is required. Property Map
Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds This property is required. Map<String>
Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

GoogleCloudAiplatformV1beta1SamplingStrategy
, GoogleCloudAiplatformV1beta1SamplingStrategyArgs

RandomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
Random sample config. Will support more sampling strategies later.
randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
Random sample config. Will support more sampling strategies later.
randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
Random sample config. Will support more sampling strategies later.
random_sample_config GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
Random sample config. Will support more sampling strategies later.
randomSampleConfig Property Map
Random sample config. Will support more sampling strategies later.

GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
, GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs

SampleRate double
Sample rate (0, 1]
SampleRate float64
Sample rate (0, 1]
sampleRate Double
Sample rate (0, 1]
sampleRate number
Sample rate (0, 1]
sample_rate float
Sample rate (0, 1]
sampleRate Number
Sample rate (0, 1]

GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
, GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponseArgs

SampleRate This property is required. double
Sample rate (0, 1]
SampleRate This property is required. float64
Sample rate (0, 1]
sampleRate This property is required. Double
Sample rate (0, 1]
sampleRate This property is required. number
Sample rate (0, 1]
sample_rate This property is required. float
Sample rate (0, 1]
sampleRate This property is required. Number
Sample rate (0, 1]

GoogleCloudAiplatformV1beta1SamplingStrategyResponse
, GoogleCloudAiplatformV1beta1SamplingStrategyResponseArgs

RandomSampleConfig This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
Random sample config. Will support more sampling strategies later.
RandomSampleConfig This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
Random sample config. Will support more sampling strategies later.
randomSampleConfig This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
Random sample config. Will support more sampling strategies later.
randomSampleConfig This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
Random sample config. Will support more sampling strategies later.
random_sample_config This property is required. GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
Random sample config. Will support more sampling strategies later.
randomSampleConfig This property is required. Property Map
Random sample config. Will support more sampling strategies later.

GoogleCloudAiplatformV1beta1ThresholdConfig
, GoogleCloudAiplatformV1beta1ThresholdConfigArgs

Value double
Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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.

GoogleCloudAiplatformV1beta1ThresholdConfigResponse
, GoogleCloudAiplatformV1beta1ThresholdConfigResponseArgs

Value This property is required. double
Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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. If this threshold config is for feature distribution distance: 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.

GoogleRpcStatusResponse
, GoogleRpcStatusResponseArgs

Code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. List<ImmutableDictionary<string, string>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
Code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. []map[string]string
A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. Integer
The status code, which should be an enum value of google.rpc.Code.
details This property is required. List<Map<String,String>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. String
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. {[key: string]: string}[]
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. string
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. int
The status code, which should be an enum value of google.rpc.Code.
details This property is required. Sequence[Mapping[str, str]]
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. str
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
code This property is required. Number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. List<Map<String>>
A list of messages that carry the error details. There is a common set of message types for APIs to use.
message This property is required. String
A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

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