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

google-native.aiplatform/v1.getModelDeploymentMonitoringJob

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

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

    Gets a ModelDeploymentMonitoringJob.

    Using getModelDeploymentMonitoringJob

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

    function getModelDeploymentMonitoringJob(args: GetModelDeploymentMonitoringJobArgs, opts?: InvokeOptions): Promise<GetModelDeploymentMonitoringJobResult>
    function getModelDeploymentMonitoringJobOutput(args: GetModelDeploymentMonitoringJobOutputArgs, opts?: InvokeOptions): Output<GetModelDeploymentMonitoringJobResult>
    def get_model_deployment_monitoring_job(location: Optional[str] = None,
                                            model_deployment_monitoring_job_id: Optional[str] = None,
                                            project: Optional[str] = None,
                                            opts: Optional[InvokeOptions] = None) -> GetModelDeploymentMonitoringJobResult
    def get_model_deployment_monitoring_job_output(location: Optional[pulumi.Input[str]] = None,
                                            model_deployment_monitoring_job_id: Optional[pulumi.Input[str]] = None,
                                            project: Optional[pulumi.Input[str]] = None,
                                            opts: Optional[InvokeOptions] = None) -> Output[GetModelDeploymentMonitoringJobResult]
    func LookupModelDeploymentMonitoringJob(ctx *Context, args *LookupModelDeploymentMonitoringJobArgs, opts ...InvokeOption) (*LookupModelDeploymentMonitoringJobResult, error)
    func LookupModelDeploymentMonitoringJobOutput(ctx *Context, args *LookupModelDeploymentMonitoringJobOutputArgs, opts ...InvokeOption) LookupModelDeploymentMonitoringJobResultOutput

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

    public static class GetModelDeploymentMonitoringJob 
    {
        public static Task<GetModelDeploymentMonitoringJobResult> InvokeAsync(GetModelDeploymentMonitoringJobArgs args, InvokeOptions? opts = null)
        public static Output<GetModelDeploymentMonitoringJobResult> Invoke(GetModelDeploymentMonitoringJobInvokeArgs args, InvokeOptions? opts = null)
    }
    public static CompletableFuture<GetModelDeploymentMonitoringJobResult> getModelDeploymentMonitoringJob(GetModelDeploymentMonitoringJobArgs args, InvokeOptions options)
    // Output-based functions aren't available in Java yet
    
    fn::invoke:
      function: google-native:aiplatform/v1:getModelDeploymentMonitoringJob
      arguments:
        # arguments dictionary

    The following arguments are supported:

    getModelDeploymentMonitoringJob Result

    The following output properties are available:

    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.
    BigqueryTables List<Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse>
    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.
    DisplayName 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.
    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.V1.Outputs.GoogleCloudAiplatformV1EncryptionSpecResponse
    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.
    Endpoint string
    Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
    Error Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    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.
    LatestMonitoringPipelineMetadata Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
    Latest triggered monitoring pipeline metadata.
    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.
    LoggingSamplingStrategy Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1SamplingStrategyResponse
    Sample Strategy for logging.
    ModelDeploymentMonitoringObjectiveConfigs List<Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse>
    The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
    ModelDeploymentMonitoringScheduleConfig Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse
    Schedule config for running the monitoring job.
    ModelMonitoringAlertConfig Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse
    Alert config for model monitoring.
    Name string
    Resource name of a ModelDeploymentMonitoringJob.
    NextScheduleTime string
    Timestamp when this monitoring pipeline will be scheduled to run for the next round.
    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.
    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.
    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'.
    StatsAnomaliesBaseDirectory Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleCloudAiplatformV1GcsDestinationResponse
    Stats anomalies base folder path.
    UpdateTime string
    Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
    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.
    BigqueryTables []GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse
    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.
    DisplayName 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.
    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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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.
    Endpoint string
    Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
    Error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    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.
    LatestMonitoringPipelineMetadata GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
    Latest triggered monitoring pipeline metadata.
    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.
    LoggingSamplingStrategy GoogleCloudAiplatformV1SamplingStrategyResponse
    Sample Strategy for logging.
    ModelDeploymentMonitoringObjectiveConfigs []GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse
    The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
    ModelDeploymentMonitoringScheduleConfig GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse
    Schedule config for running the monitoring job.
    ModelMonitoringAlertConfig GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse
    Alert config for model monitoring.
    Name string
    Resource name of a ModelDeploymentMonitoringJob.
    NextScheduleTime string
    Timestamp when this monitoring pipeline will be scheduled to run for the next round.
    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.
    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.
    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'.
    StatsAnomaliesBaseDirectory GoogleCloudAiplatformV1GcsDestinationResponse
    Stats anomalies base folder path.
    UpdateTime string
    Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
    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.
    bigqueryTables List<GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse>
    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.
    displayName 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.
    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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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.
    endpoint String
    Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
    error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    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.
    latestMonitoringPipelineMetadata GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
    Latest triggered monitoring pipeline metadata.
    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.
    loggingSamplingStrategy GoogleCloudAiplatformV1SamplingStrategyResponse
    Sample Strategy for logging.
    modelDeploymentMonitoringObjectiveConfigs List<GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse>
    The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
    modelDeploymentMonitoringScheduleConfig GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse
    Schedule config for running the monitoring job.
    modelMonitoringAlertConfig GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse
    Alert config for model monitoring.
    name String
    Resource name of a ModelDeploymentMonitoringJob.
    nextScheduleTime String
    Timestamp when this monitoring pipeline will be scheduled to run for the next round.
    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.
    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.
    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'.
    statsAnomaliesBaseDirectory GoogleCloudAiplatformV1GcsDestinationResponse
    Stats anomalies base folder path.
    updateTime String
    Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
    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.
    bigqueryTables GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse[]
    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.
    displayName 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.
    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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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.
    endpoint string
    Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
    error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    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.
    latestMonitoringPipelineMetadata GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
    Latest triggered monitoring pipeline metadata.
    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.
    loggingSamplingStrategy GoogleCloudAiplatformV1SamplingStrategyResponse
    Sample Strategy for logging.
    modelDeploymentMonitoringObjectiveConfigs GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse[]
    The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
    modelDeploymentMonitoringScheduleConfig GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse
    Schedule config for running the monitoring job.
    modelMonitoringAlertConfig GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse
    Alert config for model monitoring.
    name string
    Resource name of a ModelDeploymentMonitoringJob.
    nextScheduleTime string
    Timestamp when this monitoring pipeline will be scheduled to run for the next round.
    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.
    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.
    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'.
    statsAnomaliesBaseDirectory GoogleCloudAiplatformV1GcsDestinationResponse
    Stats anomalies base folder path.
    updateTime string
    Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
    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.
    bigquery_tables Sequence[GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse]
    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.
    display_name 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.
    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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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.
    endpoint str
    Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
    error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    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.
    latest_monitoring_pipeline_metadata GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
    Latest triggered monitoring pipeline metadata.
    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.
    logging_sampling_strategy GoogleCloudAiplatformV1SamplingStrategyResponse
    Sample Strategy for logging.
    model_deployment_monitoring_objective_configs Sequence[GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse]
    The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
    model_deployment_monitoring_schedule_config GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse
    Schedule config for running the monitoring job.
    model_monitoring_alert_config GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse
    Alert config for model monitoring.
    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.
    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.
    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.
    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'.
    stats_anomalies_base_directory GoogleCloudAiplatformV1GcsDestinationResponse
    Stats anomalies base folder path.
    update_time str
    Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
    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.
    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.
    displayName 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.
    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.
    endpoint String
    Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
    error Property Map
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    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.
    latestMonitoringPipelineMetadata Property Map
    Latest triggered monitoring pipeline metadata.
    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.
    loggingSamplingStrategy Property Map
    Sample Strategy for logging.
    modelDeploymentMonitoringObjectiveConfigs List<Property Map>
    The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
    modelDeploymentMonitoringScheduleConfig Property Map
    Schedule config for running the monitoring job.
    modelMonitoringAlertConfig Property Map
    Alert config for model monitoring.
    name String
    Resource name of a ModelDeploymentMonitoringJob.
    nextScheduleTime String
    Timestamp when this monitoring pipeline will be scheduled to run for the next round.
    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.
    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.
    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'.
    statsAnomaliesBaseDirectory Property Map
    Stats anomalies base folder path.
    updateTime String
    Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

    Supporting Types

    GoogleCloudAiplatformV1BigQueryDestinationResponse

    OutputUri 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 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 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 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 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 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.

    GoogleCloudAiplatformV1BigQuerySourceResponse

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

    GoogleCloudAiplatformV1EncryptionSpecResponse

    KmsKeyName 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 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 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 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 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 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.

    GoogleCloudAiplatformV1GcsDestinationResponse

    OutputUriPrefix 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 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 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 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 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 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.

    GoogleCloudAiplatformV1GcsSourceResponse

    Uris 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 []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 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 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 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 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.

    GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse

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

    GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse

    RunTime string
    The time that most recent monitoring pipelines that is related to this run.
    Status Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleRpcStatusResponse
    The status of the most recent monitoring pipeline.
    RunTime string
    The time that most recent monitoring pipelines that is related to this run.
    Status GoogleRpcStatusResponse
    The status of the most recent monitoring pipeline.
    runTime String
    The time that most recent monitoring pipelines that is related to this run.
    status GoogleRpcStatusResponse
    The status of the most recent monitoring pipeline.
    runTime string
    The time that most recent monitoring pipelines that is related to this run.
    status GoogleRpcStatusResponse
    The status of the most recent monitoring pipeline.
    run_time str
    The time that most recent monitoring pipelines that is related to this run.
    status GoogleRpcStatusResponse
    The status of the most recent monitoring pipeline.
    runTime String
    The time that most recent monitoring pipelines that is related to this run.
    status Property Map
    The status of the most recent monitoring pipeline.

    GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse

    DeployedModelId string
    The DeployedModel ID of the objective config.
    ObjectiveConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigResponse
    The objective config of for the modelmonitoring job of this deployed model.
    DeployedModelId string
    The DeployedModel ID of the objective config.
    ObjectiveConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigResponse
    The objective config of for the modelmonitoring job of this deployed model.
    deployedModelId String
    The DeployedModel ID of the objective config.
    objectiveConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigResponse
    The objective config of for the modelmonitoring job of this deployed model.
    deployedModelId string
    The DeployedModel ID of the objective config.
    objectiveConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigResponse
    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 GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigResponse
    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.

    GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse

    MonitorInterval 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 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 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 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 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 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.

    GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfigResponse

    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.

    GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse

    EmailAlertConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfigResponse
    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 GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfigResponse
    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 GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfigResponse
    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 GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfigResponse
    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 GoogleCloudAiplatformV1ModelMonitoringAlertConfigEmailAlertConfigResponse
    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/

    GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse

    Bigquery Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    Gcs Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    PredictionFormat string
    The storage format of the predictions generated BatchPrediction job.
    Bigquery GoogleCloudAiplatformV1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    Gcs GoogleCloudAiplatformV1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    PredictionFormat string
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    predictionFormat String
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    predictionFormat string
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    prediction_format str
    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 String
    The storage format of the predictions generated BatchPrediction job.

    GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigResponse

    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.V1.Inputs.GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    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 GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    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 GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    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 GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    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 GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    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.

    GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

    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.V1.Inputs.GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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.

    GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigResponse

    ExplanationConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigResponse
    The config for integrating with Vertex Explainable AI.
    PredictionDriftDetectionConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
    The config for drift of prediction data.
    TrainingDataset Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDatasetResponse
    Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
    TrainingPredictionSkewDetectionConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
    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.

    GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDatasetResponse

    BigquerySource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQuerySourceResponse
    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.V1.Inputs.GoogleCloudAiplatformV1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    LoggingSamplingStrategy Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SamplingStrategyResponse
    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 GoogleCloudAiplatformV1BigQuerySourceResponse
    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 GoogleCloudAiplatformV1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    LoggingSamplingStrategy GoogleCloudAiplatformV1SamplingStrategyResponse
    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 GoogleCloudAiplatformV1BigQuerySourceResponse
    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 GoogleCloudAiplatformV1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy GoogleCloudAiplatformV1SamplingStrategyResponse
    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 GoogleCloudAiplatformV1BigQuerySourceResponse
    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 GoogleCloudAiplatformV1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy GoogleCloudAiplatformV1SamplingStrategyResponse
    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 GoogleCloudAiplatformV1BigQuerySourceResponse
    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 GoogleCloudAiplatformV1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    logging_sampling_strategy GoogleCloudAiplatformV1SamplingStrategyResponse
    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.

    GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

    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.V1.Inputs.GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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 GoogleCloudAiplatformV1ThresholdConfigResponse
    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.

    GoogleCloudAiplatformV1SamplingStrategyRandomSampleConfigResponse

    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]

    GoogleCloudAiplatformV1SamplingStrategyResponse

    RandomSampleConfig GoogleCloudAiplatformV1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig GoogleCloudAiplatformV1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig GoogleCloudAiplatformV1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    random_sample_config GoogleCloudAiplatformV1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig Property Map
    Random sample config. Will support more sampling strategies later.

    GoogleCloudAiplatformV1ThresholdConfigResponse

    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.

    GoogleRpcStatusResponse

    Code int
    The status code, which should be an enum value of google.rpc.Code.
    Details 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 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 int
    The status code, which should be an enum value of google.rpc.Code.
    Details []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 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 Integer
    The status code, which should be an enum value of google.rpc.Code.
    details 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 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 number
    The status code, which should be an enum value of google.rpc.Code.
    details {[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 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 int
    The status code, which should be an enum value of google.rpc.Code.
    details 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 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 Number
    The status code, which should be an enum value of google.rpc.Code.
    details 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 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-native logo

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

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