Class ModelMonitor (1.56.0) 

ModelMonitor(
    model_monitor_name: str,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Initializer for ModelMonitor.

Parameters
Name Description
model_monitor_name str

Required. A fully-qualified model monitor resource name or model monitor ID. Example: "projects/123/locations/us-central1/modelMonitors/456" or "456" when project and location are initialized or passed.
project str

Required. Project to retrieve model monitor from. If not set, project set in aiplatform.init will be used.
location str

Required. Location to retrieve model monitor from. If not set, location set in aiplatform.init will be used.
credentials auth_credentials.Credentials

Optional. Custom credentials to use to retrieve this model monitor. 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.

update_time

Time this resource was last updated.

Methods

ModelMonitor

ModelMonitor(
    model_monitor_name: str,
    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_name: str,
    model_version_id: str,
    training_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    display_name: typing.Optional[str] = None,
    model_monitoring_schema: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.schema.ModelMonitoringSchema
    ] = 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,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    model_monitor_id: typing.Optional[str] = None,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitor

Creates a new ModelMonitor.

Parameters
Name Description
model_name str

Required. A model resource name as model monitoring target. Format: projects/{project}/locations/{location}/models/{model}
model_version_id str

Required. Model version id.
training_dataset objective.MonitoringInput

Optional. Training dataset used to train the model. It can serve as a baseline dataset to identify changes in production.
display_name str

Optional. The user-defined name of the ModelMonitor. The name can be up to 128 characters long and can comprise any UTF-8 character. Display name of the ModelMonitor.
model_monitoring_schema schema.ModelMonitoringSchema

Required for most models, but optional for Vertex AI AutoML Tables unless the schema information is not available. The Monitoring Schema specifies the model's features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, Vertex AI will be unable to extract data form the dataset.
tabular_objective_spec objective.TabularObjective

Optional. The default tabular monitoring objective spec for the model monitor. It can be overriden in the ModelMonitoringJob objective spec.
output_spec output.OutputSpec

Optional. The default monitoring metrics/logs export spec, it can be overriden in the ModelMonitoringJob output spec. If not specified, a default Google Cloud Storage bucket will be created under your project.
notification_spec notification.NotificationSpec

Optional. The default notification spec for monitoring result. It can be overriden in the ModelMonitoringJob notification spec.
explanation_spec explanation.ExplanationSpec

Optional. The default explanation spec for feature attribution monitoring. It can be overriden in the ModelMonitoringJob explanation spec.
project str

Optional. Project to retrieve model monitor from. If not set, project set in aiplatform.init will be used.
location str

Optional. Location to retrieve model monitor from. If not set, location set in aiplatform.init will be used.
credentials auth_credentials.Credentials

Optional. Custom credentials to use to create this model monitor. Overrides credentials set in aiplatform.init.
model_monitor_id str

Optional. The unique ID of the model monitor, which will become the final component of the model monitor resource name. If not specified, it will be generated by Vertex AI.
Returns
Type Description
ModelMonitor The model monitor that was created.

create_schedule

create_schedule(
    cron: str,
    target_dataset: vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_display_name: typing.Optional[str] = None,
    start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = 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,
) -> google.cloud.aiplatform_v1beta1.types.schedule.Schedule

Creates a new Scheduled run for model monitoring job.

Parameters
Name Description
cron str

Required. Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
target_dataset objective.MonitoringInput

Required. The target dataset for analysis.
display_name str

Optional. The user-defined name of the Schedule. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of the Schedule.
model_monitoring_job_display_name str

Optional. The user-defined name of the ModelMonitoringJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of the ModelMonitoringJob.
start_time timestamp_pb2.Timestamp

Optional. Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
end_time timestamp_pb2.Timestamp

Optional. Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when the end_time is reached. If not specified, new runs will keep getting scheduled until this Schedule is paused or deleted. Already scheduled runs will be allowed to complete. Unset if not specified.
tabular_objective_spec objective.TabularObjective

Optional. The tabular monitoring objective spec. If not set, the default tabular objective spec in ModelMonitor will be used. You must either set here or set the default one in the ModelMonitor.
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
Schedule The created schedule.

delete

delete(force: bool = False, sync: bool = True) -> None

Force delete the model monitor.

Parameters
Name Description
force bool

Required. If force is set to True, all schedules on this ModelMonitor will be deleted first. Default is False.
sync bool

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Default is True.

delete_model_monitoring_job

delete_model_monitoring_job(model_monitoring_job_name: str) -> None

Delete a model monitoring job.

Parameter
Name Description
model_monitoring_job_name str

Required. The resource name of the model monitoring job that needs to be deleted. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job} or {model_monitoring_job}

delete_schedule

delete_schedule(schedule_name: str) -> None

Deletes an existing Schedule.

Parameter
Name Description
schedule_name str

Required. The resource name of schedule that needs to be deleted. Format: projects/{project}/locations/{location}/schedules/{schedule} or {schedule}

get_model_monitoring_job

get_model_monitoring_job(
    model_monitoring_job_name: str,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJob

Get the specified ModelMonitoringJob.

Parameter
Name Description
model_monitoring_job_name str

Required. The resource name of the ModelMonitoringJob that is needed. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job} or {model_monitoring_job}
Returns
Type Description
ModelMonitoringJob The model monitoring job get.

get_schedule

get_schedule(
    schedule_name: str,
) -> google.cloud.aiplatform_v1beta1.types.schedule.Schedule

Gets an existing Schedule.

Parameter
Name Description
schedule_name str

Required. The resource name of schedule that needs to be fetched. Format: projects/{project}/locations/{location}/schedules/{schedule} or {schedule}
Returns
Type Description
Schedule The schedule requested.

get_schema

get_schema() -> (
    google.cloud.aiplatform_v1beta1.types.model_monitor.ModelMonitoringSchema
)

Get the schema of the model monitor.

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.

Example Usage:

aiplatform.BatchPredictionJobs.list( filter='state="JOB_STATE_SUCCEEDED" AND display_name="my_job"', )

aiplatform.Model.list(order_by="create_time desc, display_name")

Parameters
Name Description
filter str

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.
order_by str

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name, create_time, update_time
project str

Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.
location str

Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.
credentials auth_credentials.Credentials

Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.
parent str

Optional. The parent resource name if any to retrieve list from.

list_jobs

list_jobs(
    page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None
) -> ListJobsResponse.list_jobs

List ModelMonitoringJobs.

Parameters
Name Description
page_size int

Optional. The standard page list size.
page_token str

Optional. A page token received from a previous call.
Returns
Type Description
ListJobsResponse.list_jobs The list model monitoring jobs responses.

list_schedules

list_schedules(
    filter: typing.Optional[str] = None,
    page_size: typing.Optional[int] = None,
    page_token: typing.Optional[str] = None,
) -> ListSchedulesResponse.list_schedules

List Schedules.

Parameters
Name Description
filter str

Optional. Lists the Schedules that match the filter expression. The following fields are supported: - display_name: Supports =, != comparisons, and : wildcard. - state: Supports = and != comparisons. - request: Supports existence of the <request_type> check. (e.g. create_pipeline_job_request: --> Schedule has create_pipeline_job_request). - create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. - start_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. - end_time: Supports =, !=, <, >, <=, >= comparisons and : existence check. Values must be in RFC 3339 format. - next_run_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. Filter expressions can be combined together using logical operators (NOT, AND & OR). The syntax to define filter expression is based on https://google.aip.dev/160.
page_size int

Optional. The standard page list size.
page_token str

Optional. A page token received from a previous call.
Returns
Type Description
MetricsSearchResponse The model monitoring stats results.

pause_schedule

pause_schedule(schedule_name: str) -> None

Pauses an existing Schedule.

Parameter
Name Description
schedule_name str

Required. The resource name of schedule that needs to be paused. Format: projects/{project}/locations/{location}/schedules/{schedule} or {schedule}

resume_schedule

resume_schedule(schedule_name: str) -> None

Resumes an existing Schedule.

Parameter
Name Description
schedule_name str

Required. The resource name of schedule that needs to be resumed. Format: projects/{project}/locations/{location}/schedules/{schedule} or {schedule}

run

run(
    target_dataset: vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_id: typing.Optional[str] = None,
    sync: typing.Optional[bool] = True,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = 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
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. Display name of the ModelMonitoringJob.
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.
sync bool

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Default is True.
tabular_objective_spec objective.TabularObjective

Optional. The tabular monitoring objective spec for the model monitoring job.
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_config 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.

search_alerts

search_alerts(
    stats_name: typing.Optional[str] = None,
    objective_type: typing.Optional[str] = None,
    model_monitoring_job_name: typing.Optional[str] = None,
    start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    page_size: typing.Optional[int] = None,
    page_token: typing.Optional[str] = None,
) -> typing.Dict[str, typing.Any]

Search ModelMonitoringAlerts.

Parameters
Name Description
stats_name str

Optional. The stats name filter for the search, if not set, all stats will be returned. For tabular models, provide the name of the feature to return alerts from.
objective_type str

Optional. Return alerts from one of the supported monitoring objectives: raw-feature-drift prediction-output-drift feature-attribution
model_monitoring_job_name str

Optional. The resource name of a particular model monitoring job that the user wants to search metrics result from. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}
start_time timestamp_pb2.Timestamp

Optional. Inclusive start of the time interval for which alerts should be returned.
end_time timestamp_pb2.Timestamp

Optional. Exclusive end of the time interval for which alerts should be returned.
page_size int

Optional. The standard page list size.
page_token str

Optional. A page token received from a previous call.
Returns
Type Description
AlertsSearchResponse The model monitoring alerts results.

search_metrics

search_metrics(
    stats_name: typing.Optional[str] = None,
    objective_type: typing.Optional[str] = None,
    model_monitoring_job_name: typing.Optional[str] = None,
    schedule_name: typing.Optional[str] = None,
    algorithm: typing.Optional[str] = None,
    start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    page_size: typing.Optional[int] = None,
    page_token: typing.Optional[str] = None,
) -> MetricsSearchResponse.monitoring_stats

Search ModelMonitoringStats.

Parameters
Name Description
stats_name str

Optional. The stats name filter for the search, if not set, all stats will be returned. For tabular model it's the feature name.
objective_type str

Optional. One of the supported monitoring objectives: raw-feature-drift prediction-output-drift feature-attribution
model_monitoring_job_name str

Optional. The resource name of a particular model monitoring job that the user wants to search metrics result from. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}
schedule_name str

Optional. The resource name of a particular model monitoring schedule that the user wants to search metrics result from. Format: projects/{project}/locations/{location}/schedules/{schedule}
algorithm str

Optional. The algorithm type filter for the search, eg: jensen_shannon_divergence, l_infinity.
start_time timestamp_pb2.Timestamp

Optional. Inclusive start of the time interval for which results should be returned.
end_time timestamp_pb2.Timestamp

Optional. Exclusive end of the time interval for which results should be returned.
page_size int

Optional. The standard page list size.
page_token str

Optional. A page token received from a previous call.
Returns
Type Description
MetricsSearchResponse The model monitoring stats results.

show_feature_attribution_drift_stats

show_feature_attribution_drift_stats(model_monitoring_job_name: str) -> None

The method to visualize the feature attribution drift result from a model monitoring job as a histogram chart and a table.

Parameter
Name Description
model_monitoring_job_name str

Required. The resource name of model monitoring job to show the feature attribution drift stats from. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job} or {model_monitoring_job}

show_feature_drift_stats

show_feature_drift_stats(model_monitoring_job_name: str) -> None

The method to visualize the feature drift result from a model monitoring job as a histogram chart and a table.

Parameter
Name Description
model_monitoring_job_name str

Required. The resource name of model monitoring job to show the drift stats from. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job} or {model_monitoring_job}

show_output_drift_stats

show_output_drift_stats(model_monitoring_job_name: str) -> None

The method to visualize the prediction output drift result from a model monitoring job as a histogram chart and a table.

Parameter
Name Description
model_monitoring_job_name str

Required. The resource name of model monitoring job to show the drift stats from. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job} or {model_monitoring_job}

to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

update

update(
    display_name: typing.Optional[str] = None,
    training_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    model_monitoring_schema: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.schema.ModelMonitoringSchema
    ] = 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.ModelMonitor

Updates an existing ModelMonitor.

Parameters
Name Description
display_name str

Optional. The user-defined name of the ModelMonitor. The name can be up to 128 characters long and can comprise any UTF-8 character. Display name of the ModelMonitor.
training_dataset objective.MonitoringInput

Optional. Training dataset used to train the model. It can serve as a baseline dataset to identify changes in production.
model_monitoring_schema schema.ModelMonitoringSchema

Optional. The Monitoring Schema specifies the model's features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, Vertex AI will be unable to extract data form the dataset.
tabular_objective_spec objective.TabularObjective

Optional. The default tabular monitoring objective spec for the model monitor. It can be overriden in the ModelMonitoringJob objective spec.
output_spec output.OutputSpec

Optional. The default monitoring metrics/logs export spec, it can be overriden in the ModelMonitoringJob output spec.
notification_spec notification.NotificationSpec

Optional. The default notification spec for monitoring result. It can be overriden in the ModelMonitoringJob notification spec.
explanation_spec explanation.ExplanationSpec

Optional. The default explanation spec for feature attribution monitoring. It can be overriden in the ModelMonitoringJob explanation spec.
Returns
Type Description
ModelMonitor The updated model monitor.

update_schedule

update_schedule(
    schedule_name: str,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_display_name: typing.Optional[str] = None,
    cron: typing.Optional[str] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    target_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,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
) -> google.cloud.aiplatform_v1beta1.types.schedule.Schedule

Updates an existing Schedule.

Parameters
Name Description
schedule_name str

Required. The resource name of schedule that needs to be updated. Format: projects/{project}/locations/{location}/schedules/{schedule} or {schedule}
display_name str

Optional. The user-defined name of the Schedule. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of the Schedule.
model_monitoring_job_display_name str

Optional. The user-defined display name of the ModelMonitoringJob that needs to be updated.
cron str

Optional. Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
baseline_dataset objective.MonitoringInput

Optional. The baseline dataset for monitoring job.
target_dataset objective.MonitoringInput

Optional. The target dataset for analysis.
tabular_objective_spec objective.TabularObjective

Optional. The tabular monitoring objective spec.
output_spec output.OutputSpec

Optional. The monitoring metrics/logs export spec.
notification_spec notification.NotificationSpec

Optional. The notification spec for monitoring result.
explanation_spec explanation.ExplanationSpec

Optional. The explanation spec for feature attribution monitoring.
end_time timestamp_pb2.Timestamp

Optional. Timestamp after which no new runs can be scheduled.
Returns
Type Description
Schedule The updated schedule.

wait

wait()

Helper method that blocks until all futures are complete.