Google Cloud Ai Platform V1 Client - Class ModelDeploymentMonitoringJob (0.10.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class ModelDeploymentMonitoringJob.

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

Generated from protobuf message google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob

Methods

__construct

Constructor.

Parameters
Name	Description
`data`	`array` Optional. Data for populating the Message object.
`↳ name`	`string` Output only. Resource name of a ModelDeploymentMonitoringJob.
`↳ display_name`	`string` Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
`↳ endpoint`	`string` Required. Endpoint resource name. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
`↳ state`	`int` Output only. 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'.
`↳ schedule_state`	`int` Output only. Schedule state when the monitoring job is in Running state.
`↳ latest_monitoring_pipeline_metadata`	`Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob\LatestMonitoringPipelineMetadata` Output only. Latest triggered monitoring pipeline metadata.
`↳ model_deployment_monitoring_objective_configs`	`array<Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringObjectiveConfig>` Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
`↳ model_deployment_monitoring_schedule_config`	`Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringScheduleConfig` Required. Schedule config for running the monitoring job.
`↳ logging_sampling_strategy`	`Google\Cloud\AIPlatform\V1\SamplingStrategy` Required. Sample Strategy for logging.
`↳ model_monitoring_alert_config`	`Google\Cloud\AIPlatform\V1\ModelMonitoringAlertConfig` Alert config for model monitoring.
`↳ predict_instance_schema_uri`	`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.
`↳ sample_predict_instance`	`Google\Protobuf\Value` 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.
`↳ analysis_instance_schema_uri`	`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.
`↳ bigquery_tables`	`array<Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringBigQueryTable>` Output only. 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
`↳ log_ttl`	`Google\Protobuf\Duration` 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.
`↳ labels`	`array\|Google\Protobuf\Internal\MapField` 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.
`↳ create_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
`↳ update_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
`↳ next_schedule_time`	`Google\Protobuf\Timestamp` Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.
`↳ stats_anomalies_base_directory`	`Google\Cloud\AIPlatform\V1\GcsDestination` Stats anomalies base folder path.
`↳ encryption_spec`	`Google\Cloud\AIPlatform\V1\EncryptionSpec` 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.
`↳ 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.
`↳ error`	`Google\Rpc\Status` Output only. Only populated when the job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.

getName

Output only. Resource name of a ModelDeploymentMonitoringJob.

Generated from protobuf field string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`string`

setName

Output only. Resource name of a ModelDeploymentMonitoringJob.

Generated from protobuf field string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getDisplayName

Required. 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.

Generated from protobuf field string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
`string`

setDisplayName

Required. 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.

Generated from protobuf field string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getEndpoint

Required. Endpoint resource name.

Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Generated from protobuf field string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = {

Returns
Type	Description
`string`

setEndpoint

Required. Endpoint resource name.

Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Generated from protobuf field string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = {

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getState

Output only. 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'.

Generated from protobuf field .google.cloud.aiplatform.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`int`

setState

Output only. The detailed state of the monitoring job.

Generated from protobuf field .google.cloud.aiplatform.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`int`

Returns
Type	Description
`$this`

getScheduleState

Output only. Schedule state when the monitoring job is in Running state.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`int`

setScheduleState

Output only. Schedule state when the monitoring job is in Running state.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`int`

Returns
Type	Description
`$this`

getLatestMonitoringPipelineMetadata

Output only. Latest triggered monitoring pipeline metadata.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata latest_monitoring_pipeline_metadata = 25 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob\LatestMonitoringPipelineMetadata\|null`

hasLatestMonitoringPipelineMetadata

clearLatestMonitoringPipelineMetadata

setLatestMonitoringPipelineMetadata

Output only. Latest triggered monitoring pipeline metadata.

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob\LatestMonitoringPipelineMetadata`

Returns
Type	Description
`$this`

getModelDeploymentMonitoringObjectiveConfigs

Required. The config for monitoring objectives. This is a per DeployedModel config.

Each DeployedModel needs to be configured separately.

Generated from protobuf field repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setModelDeploymentMonitoringObjectiveConfigs

Required. The config for monitoring objectives. This is a per DeployedModel config.

Each DeployedModel needs to be configured separately.

Parameter
Name	Description
`var`	`array<Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringObjectiveConfig>`

Returns
Type	Description
`$this`

getModelDeploymentMonitoringScheduleConfig

Required. Schedule config for running the monitoring job.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringScheduleConfig\|null`

hasModelDeploymentMonitoringScheduleConfig

clearModelDeploymentMonitoringScheduleConfig

setModelDeploymentMonitoringScheduleConfig

Required. Schedule config for running the monitoring job.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringScheduleConfig`

Returns
Type	Description
`$this`

getLoggingSamplingStrategy

Required. Sample Strategy for logging.

Generated from protobuf field .google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\SamplingStrategy\|null`

hasLoggingSamplingStrategy

clearLoggingSamplingStrategy

setLoggingSamplingStrategy

Required. Sample Strategy for logging.

Generated from protobuf field .google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\SamplingStrategy`

Returns
Type	Description
`$this`

getModelMonitoringAlertConfig

Alert config for model monitoring.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\ModelMonitoringAlertConfig\|null`

hasModelMonitoringAlertConfig

clearModelMonitoringAlertConfig

setModelMonitoringAlertConfig

Alert config for model monitoring.

Generated from protobuf field .google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\ModelMonitoringAlertConfig`

Returns
Type	Description
`$this`

getPredictInstanceSchemaUri

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.

Generated from protobuf field string predict_instance_schema_uri = 9;

Returns
Type	Description
`string`

setPredictInstanceSchemaUri

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.

Generated from protobuf field string predict_instance_schema_uri = 9;

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getSamplePredictInstance

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.

Generated from protobuf field .google.protobuf.Value sample_predict_instance = 19;

Returns
Type	Description
`Google\Protobuf\Value\|null`

hasSamplePredictInstance

clearSamplePredictInstance

setSamplePredictInstance

Generated from protobuf field .google.protobuf.Value sample_predict_instance = 19;

Parameter
Name	Description
`var`	`Google\Protobuf\Value`

Returns
Type	Description
`$this`

getAnalysisInstanceSchemaUri

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.

Generated from protobuf field string analysis_instance_schema_uri = 16;

Returns
Type	Description
`string`

setAnalysisInstanceSchemaUri

YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze.

Generated from protobuf field string analysis_instance_schema_uri = 16;

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getBigqueryTables

Output only. 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:

Training data logging predict request/response
Serving data logging predict request/response

Generated from protobuf field repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Google\Protobuf\Internal\RepeatedField`

setBigqueryTables

Output only. 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:

Training data logging predict request/response
Serving data logging predict request/response

Generated from protobuf field repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`array<Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringBigQueryTable>`

Returns
Type	Description
`$this`

getLogTtl

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.

Generated from protobuf field .google.protobuf.Duration log_ttl = 17;

Returns
Type	Description
`Google\Protobuf\Duration\|null`

hasLogTtl

clearLogTtl

setLogTtl

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.

Generated from protobuf field .google.protobuf.Duration log_ttl = 17;

Parameter
Name	Description
`var`	`Google\Protobuf\Duration`

Returns
Type	Description
`$this`

getLabels

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.

Generated from protobuf field map<string, string> labels = 11;

Returns
Type	Description
`Google\Protobuf\Internal\MapField`

setLabels

The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob.

Generated from protobuf field map<string, string> labels = 11;

Parameter
Name	Description
`var`	`array\|Google\Protobuf\Internal\MapField`

Returns
Type	Description
`$this`

getCreateTime

Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

Generated from protobuf field .google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasCreateTime

clearCreateTime

setCreateTime

Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

Generated from protobuf field .google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

getUpdateTime

Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

Generated from protobuf field .google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasUpdateTime

clearUpdateTime

setUpdateTime

Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

Generated from protobuf field .google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

getNextScheduleTime

Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

Generated from protobuf field .google.protobuf.Timestamp next_schedule_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Google\Protobuf\Timestamp\|null`

hasNextScheduleTime

clearNextScheduleTime

setNextScheduleTime

Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

Generated from protobuf field .google.protobuf.Timestamp next_schedule_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`Google\Protobuf\Timestamp`

Returns
Type	Description
`$this`

getStatsAnomaliesBaseDirectory

Stats anomalies base folder path.

Generated from protobuf field .google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\GcsDestination\|null`

hasStatsAnomaliesBaseDirectory

clearStatsAnomaliesBaseDirectory

setStatsAnomaliesBaseDirectory

Stats anomalies base folder path.

Generated from protobuf field .google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\GcsDestination`

Returns
Type	Description
`$this`

getEncryptionSpec

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.

Generated from protobuf field .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 21;

Returns
Type	Description
`Google\Cloud\AIPlatform\V1\EncryptionSpec\|null`

hasEncryptionSpec

clearEncryptionSpec

setEncryptionSpec

Generated from protobuf field .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 21;

Parameter
Name	Description
`var`	`Google\Cloud\AIPlatform\V1\EncryptionSpec`

Returns
Type	Description
`$this`

getEnableMonitoringPipelineLogs

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.

Generated from protobuf field bool enable_monitoring_pipeline_logs = 22;

Returns
Type	Description
`bool`

setEnableMonitoringPipelineLogs

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.

Generated from protobuf field bool enable_monitoring_pipeline_logs = 22;

Parameter
Name	Description
`var`	`bool`

Returns
Type	Description
`$this`

getError

Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Generated from protobuf field .google.rpc.Status error = 23 [(.google.api.field_behavior) = OUTPUT_ONLY];

Returns
Type	Description
`Google\Rpc\Status\|null`

hasError

clearError

setError

Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Generated from protobuf field .google.rpc.Status error = 23 [(.google.api.field_behavior) = OUTPUT_ONLY];

Parameter
Name	Description
`var`	`Google\Rpc\Status`

Returns
Type	Description
`$this`