Google Cloud Native v0.32.0, Nov 29 23

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

google-native.aiplatform/v1beta1.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

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.

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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)
public static Output<GetModelDeploymentMonitoringJobResult> getModelDeploymentMonitoringJob(GetModelDeploymentMonitoringJobArgs args, InvokeOptions options)

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

The following arguments are supported:

Location string
ModelDeploymentMonitoringJobId string
Project string

Location string
ModelDeploymentMonitoringJobId string
Project string

location String
modelDeploymentMonitoringJobId String
project String

location string
modelDeploymentMonitoringJobId string
project string

location str
model_deployment_monitoring_job_id str
project str

location String
modelDeploymentMonitoringJobId String
project String

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.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.
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.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1EncryptionSpecResponse: 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.V1Beta1.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.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse: 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.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Sample Strategy for logging.
ModelDeploymentMonitoringObjectiveConfigs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse>: The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
ModelDeploymentMonitoringScheduleConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse: Schedule config for running the monitoring job.
ModelMonitoringAlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: 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.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse: 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 []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.
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 GoogleCloudAiplatformV1beta1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse: 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 GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Sample Strategy for logging.
ModelDeploymentMonitoringObjectiveConfigs []GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse: The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
ModelDeploymentMonitoringScheduleConfig GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse: Schedule config for running the monitoring job.
ModelMonitoringAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1GcsDestinationResponse: 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<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.
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 GoogleCloudAiplatformV1beta1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse: 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 GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Sample Strategy for logging.
modelDeploymentMonitoringObjectiveConfigs List<GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse>: The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
modelDeploymentMonitoringScheduleConfig GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse: Schedule config for running the monitoring job.
modelMonitoringAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1GcsDestinationResponse: 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 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.
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 GoogleCloudAiplatformV1beta1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse: 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 GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Sample Strategy for logging.
modelDeploymentMonitoringObjectiveConfigs GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse[]: The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
modelDeploymentMonitoringScheduleConfig GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse: Schedule config for running the monitoring job.
modelMonitoringAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1GcsDestinationResponse: 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[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.
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 GoogleCloudAiplatformV1beta1EncryptionSpecResponse: 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 GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse: 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 GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Sample Strategy for logging.
model_deployment_monitoring_objective_configs Sequence[GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse]: The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
model_deployment_monitoring_schedule_config GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse: Schedule config for running the monitoring job.
model_monitoring_alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1GcsDestinationResponse: 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

GoogleCloudAiplatformV1beta1BigQueryDestinationResponse

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.

GoogleCloudAiplatformV1beta1BigQuerySourceResponse

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.

GoogleCloudAiplatformV1beta1EncryptionSpecResponse

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.

GoogleCloudAiplatformV1beta1GcsDestinationResponse

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.

GoogleCloudAiplatformV1beta1GcsSourceResponse

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.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse

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.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse

RunTime string: The time that most recent monitoring pipelines that is related to this run.
Status Pulumi.GoogleNative.Aiplatform.V1Beta1.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.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse

DeployedModelId string: The DeployedModel ID of the objective config.
ObjectiveConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse: The objective config of for the modelmonitoring job of this deployed model.

DeployedModelId string: The DeployedModel ID of the objective config.
ObjectiveConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse: The objective config of for the modelmonitoring job of this deployed model.

deployedModelId String: The DeployedModel ID of the objective config.
objectiveConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse: The objective config of for the modelmonitoring job of this deployed model.

deployedModelId string: The DeployedModel ID of the objective config.
objectiveConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse: 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.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse

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.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse

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.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse

EmailAlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: 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/

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse

Bigquery Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
Gcs Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
PredictionFormat string: The storage format of the predictions generated BatchPrediction job.

Bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
Gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
PredictionFormat string: The storage format of the predictions generated BatchPrediction job.

bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
predictionFormat String: The storage format of the predictions generated BatchPrediction job.

bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
predictionFormat string: The storage format of the predictions generated BatchPrediction job.

bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: 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.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse

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.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: 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 GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: 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.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

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.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 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 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 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 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 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 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 {[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 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 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.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse

ExplanationConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
PredictionDriftDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
TrainingDataset Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
TrainingPredictionSkewDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

ExplanationConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
PredictionDriftDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
TrainingDataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
TrainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

explanationConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
trainingDataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

explanationConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
trainingDataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

explanation_config GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
prediction_drift_detection_config GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
training_dataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
training_prediction_skew_detection_config GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: 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.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse

BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceResponse: 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.GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyResponse: 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 GoogleCloudAiplatformV1beta1BigQuerySourceResponse: 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 GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: 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 GoogleCloudAiplatformV1beta1BigQuerySourceResponse: 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 GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: 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 GoogleCloudAiplatformV1beta1BigQuerySourceResponse: 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 GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: 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 GoogleCloudAiplatformV1beta1BigQuerySourceResponse: 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 GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
logging_sampling_strategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: 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.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

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.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 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 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 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 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 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 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 {[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 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 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.

GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse

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]

GoogleCloudAiplatformV1beta1SamplingStrategyResponse

RandomSampleConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

RandomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

random_sample_config GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

randomSampleConfig Property Map: Random sample config. Will support more sampling strategies later.

GoogleCloudAiplatformV1beta1ThresholdConfigResponse

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

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

pulumi/pulumi-google-native

google-native.aiplatform/v1beta1.getModelDeploymentMonitoringJob

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Using getModelDeploymentMonitoringJob

getModelDeploymentMonitoringJob Result

Supporting Types

GoogleCloudAiplatformV1beta1BigQueryDestinationResponse

GoogleCloudAiplatformV1beta1BigQuerySourceResponse

GoogleCloudAiplatformV1beta1EncryptionSpecResponse

GoogleCloudAiplatformV1beta1GcsDestinationResponse

GoogleCloudAiplatformV1beta1GcsSourceResponse

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTableResponse

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfigResponse

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse

GoogleCloudAiplatformV1beta1SamplingStrategyResponse

GoogleCloudAiplatformV1beta1ThresholdConfigResponse

GoogleRpcStatusResponse

Package Details

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