google-native.aiplatform/v1.getModelDeploymentMonitoringJob

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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/

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.

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.

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.

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 GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigResponse

The config for integrating with Vertex Explainable AI.

PredictionDriftDetectionConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

The config for drift of prediction data.

TrainingDataset GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDatasetResponse

Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.

TrainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

The config for skew between training data and prediction data.

explanationConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigResponse

The config for integrating with Vertex Explainable AI.

predictionDriftDetectionConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

The config for drift of prediction data.

trainingDataset GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDatasetResponse

Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.

trainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

The config for skew between training data and prediction data.

explanationConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigResponse

The config for integrating with Vertex Explainable AI.

predictionDriftDetectionConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

The config for drift of prediction data.

trainingDataset GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDatasetResponse

Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.

trainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

The config for skew between training data and prediction data.

explanation_config GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigExplanationConfigResponse

The config for integrating with Vertex Explainable AI.

prediction_drift_detection_config GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

The config for drift of prediction data.

training_dataset GoogleCloudAiplatformV1ModelMonitoringObjectiveConfigTrainingDatasetResponse

Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.

training_prediction_skew_detection_config 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.

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.

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.

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]

RandomSampleConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.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.

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.

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.

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.