MonitoringSchedule

sagemaker.services.k8s.aws/v1alpha1

Metadata#

A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html).

Spec#

monitoringScheduleConfig: 
  monitoringJobDefinition: 
    baselineConfig: 
      baseliningJobName: string
      constraintsResource: 
        s3URI: string
      statisticsResource: 
        s3URI: string
    environment: {}
    monitoringAppSpecification: 
      containerArguments:
      - string
      containerEntrypoint:
      - string
      imageURI: string
      postAnalyticsProcessorSourceURI: string
      recordPreprocessorSourceURI: string
    monitoringInputs:
      endpointInput: 
        endTimeOffset: string
        endpointName: string
        excludeFeaturesAttribute: string
        featuresAttribute: string
        inferenceAttribute: string
        localPath: string
        probabilityAttribute: string
        probabilityThresholdAttribute: number
        s3DataDistributionType: string
        s3InputMode: string
        startTimeOffset: string
    monitoringOutputConfig: 
      kmsKeyID: string
      monitoringOutputs:
        s3Output: 
          localPath: string
          s3URI: string
          s3UploadMode: string
    monitoringResources: 
      clusterConfig: 
        instanceCount: integer
        instanceType: string
        volumeKMSKeyID: string
        volumeSizeInGB: integer
    networkConfig: 
      enableInterContainerTrafficEncryption: boolean
      enableNetworkIsolation: boolean
      vpcConfig: 
        securityGroupIDs:
        - string
        subnets:
        - string
    roleARN: string
    stoppingCondition: 
      maxRuntimeInSeconds: integer
  monitoringJobDefinitionName: string
  monitoringType: string
  scheduleConfig: 
    dataAnalysisEndTime: string
    dataAnalysisStartTime: string
    scheduleExpression: string
monitoringScheduleName: string
tags:
- key: string
  value: string

You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html).

For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html). For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy (https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf). || tags.[].key
Optional | string
| | tags.[].value
Optional | string
|

Status#

ackResourceMetadata: 
  arn: string
  ownerAccountID: string
  region: string
conditions:
- lastTransitionTime: string
  message: string
  reason: string
  status: string
  type: string
creationTime: string
failureReason: string
lastModifiedTime: string
lastMonitoringExecutionSummary: 
  creationTime: string
  endpointName: string
  failureReason: string
  lastModifiedTime: string
  monitoringExecutionStatus: string
  monitoringJobDefinitionName: string
  monitoringScheduleName: string
  monitoringType: string
  processingJobARN: string
  scheduledTime: string
monitoringScheduleStatus: string