model_deployment_monitoring_jobs

Overview

Name	model_deployment_monitoring_jobs
Type	Resource
Id	google.aiplatform.model_deployment_monitoring_jobs

Fields

Name	Datatype	Description
name	string	Output only. Resource name of a ModelDeploymentMonitoringJob.
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	array	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
createTime	string	Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
displayName	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.
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	object	Represents a customer-managed encryption key spec that can be applied to a top-level resource.
endpoint	string	Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
error	object	The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide.
labels	object	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	object	All metadata of most recent monitoring pipelines.
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	object	Sampling Strategy for logging, can be for both training and prediction dataset.
modelDeploymentMonitoringObjectiveConfigs	array	Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
modelDeploymentMonitoringScheduleConfig	object	The config for scheduling monitoring job.
modelMonitoringAlertConfig	object
nextScheduleTime	string	Output only. 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	Output only. Schedule state when the monitoring job is in Running state.
state	string	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'.
statsAnomaliesBaseDirectory	object	The Google Cloud Storage location where the output is to be written to.
updateTime	string	Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

Methods

Name	Accessible by	Required Params	Description
get	SELECT	locationsId, modelDeploymentMonitoringJobsId, projectsId	Gets a ModelDeploymentMonitoringJob.
list	SELECT	locationsId, projectsId	Lists ModelDeploymentMonitoringJobs in a Location.
create	INSERT	locationsId, projectsId	Creates a ModelDeploymentMonitoringJob. It will run periodically on a configured interval.
delete	DELETE	locationsId, modelDeploymentMonitoringJobsId, projectsId	Deletes a ModelDeploymentMonitoringJob.
_list	EXEC	locationsId, projectsId	Lists ModelDeploymentMonitoringJobs in a Location.
patch	EXEC	locationsId, modelDeploymentMonitoringJobsId, projectsId	Updates a ModelDeploymentMonitoringJob.
pause	EXEC	locationsId, modelDeploymentMonitoringJobsId, projectsId	Pauses a ModelDeploymentMonitoringJob. If the job is running, the server makes a best effort to cancel the job. Will mark ModelDeploymentMonitoringJob.state to 'PAUSED'.
resume	EXEC	locationsId, modelDeploymentMonitoringJobsId, projectsId	Resumes a paused ModelDeploymentMonitoringJob. It will start to run from next scheduled time. A deleted ModelDeploymentMonitoringJob can't be resumed.
search_model_deployment_monitoring_stats_anomalies	EXEC	locationsId, modelDeploymentMonitoringJobsId, projectsId	Searches Model Monitoring Statistics generated within a given time window.