Class ModelMonitoringJob (1.54.1)

ModelMonitoringJob(
    model_monitoring_job_name: str,
    model_monitor_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Initializer for ModelMonitoringJob.

Example Usage:

 my_monitoring_job = aiplatform.ModelMonitoringJob(
     model_monitoring_job_name='projects/123/locations/us-central1/modelMonitors/\
     my_model_monitor_id/modelMonitoringJobs/my_monitoring_job_id'
 )
 or
 my_monitoring_job = aiplatform.aiplatform.ModelMonitoringJob(
     model_monitoring_job_name='my_monitoring_job_id',
     model_monitor_id='my_model_monitor_id',
 )

Parameters
Name	Description
`model_monitoring_job_name`	`str` Required. The resource name for the Model Monitoring Job if provided alone, or the model monitoring job id if provided with model_monitor_id.
`model_monitor_id`	`str` Optional. The model monitor id depends on the way of initializing ModelMonitoringJob.
`project`	`str` Required. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used.
`location`	`str` Required. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used.
`credentials`	`auth_credentials.Credentials` Optional. Custom credentials to use to init model monitoring job. Overrides credentials set in aiplatform.init.

Properties

create_time

Time this resource was created.

display_name

Display name of this resource.

encryption_spec

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

gca_resource

The underlying resource proto representation.

labels

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

name

Name of this resource.

resource_name

Full qualified resource name.

state

Fetch Job again and return the current JobState.

Returns
Type	Description
`state (job_state.JobState)`	Enum that describes the state of a Model Monitoring Job.

update_time

Time this resource was last updated.

Methods

ModelMonitoringJob

ModelMonitoringJob(
    model_monitoring_job_name: str,
    model_monitor_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Initializes class with project, location, and api_client.

Parameters
Name	Description
`project`	`str` Optional. Project of the resource noun.
`location`	`str` Optional. The location of the resource noun.
`credentials`	`google.auth.credentials.Credentials` Optional. custom credentials to use when accessing interacting with resource noun.
`resource_name`	`str` A fully-qualified resource name or ID.

create

create(
    model_monitor_name: typing.Optional[str] = None,
    target_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    output_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec
    ] = None,
    notification_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec
    ] = None,
    explanation_spec: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec
    ] = None,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJob

Creates a new ModelMonitoringJob.

Parameters
Name	Description
`model_monitor_name`	`str` Required. The parent model monitor resource name. Format: `projects/{project}/locations/{location}/modelMonitors/{model_monitor}`
`target_dataset`	`objective.MonitoringInput` Required. The target dataset for analysis.
`display_name`	`str` Optional. The user-defined name of the ModelMonitoringJob. The name can be up to 128 characters long and can comprise any UTF-8 character.
`model_monitoring_job_id`	`str` Optional. The unique ID of the model monitoring job run, which will become the final component of the model monitoring job resource name. The maximum length is 63 characters, and valid characters are /^a-z?$/. If not specified, it will be generated by Vertex AI.
`project`	`str` Required. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used.
`location`	`str` Required. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used.
`credentials`	`auth_credentials.Credentials` Optional. Custom credentials to use to create model monitoring job. Overrides credentials set in aiplatform.init.
`baseline_dataset`	`objective.MonitoringInput` Optional. The baseline dataset for monitoring job. If not set, the training dataset in ModelMonitor will be used as baseline dataset.
`output_spec`	`output.OutputSpec` Optional. The monitoring metrics/logs export spec. If not set, will use the default output_spec defined in ModelMonitor.
`notification_spec`	`notification.NotificationSpec` Optional. The notification spec for monitoring result. If not set, will use the default notification_spec defined in ModelMonitor.
`explanation_spec`	`explanation.ExplanationSpec` Optional. The explanation spec for feature attribution monitoring. If not set, will use the default explanation_spec defined in ModelMonitor.

Returns
Type	Description
`ModelMonitoringJob`	The model monitoring job that was created.

delete

delete() -> None

Deletes an Model Monitoring Job.

done

done() -> bool

Method indicating whether a job has completed.

list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    parent: typing.Optional[str] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Vertex AI Resource.