Create Model Quality Job Definition

sagemaker_create_model_quality_job_definition

R Documentation

Creates a definition for a job that monitors model quality and drift

Description

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

Usage

sagemaker_create_model_quality_job_definition(JobDefinitionName,
  ModelQualityBaselineConfig, ModelQualityAppSpecification,
  ModelQualityJobInput, ModelQualityJobOutputConfig, JobResources,
  NetworkConfig, RoleArn, StoppingCondition, Tags)

Arguments

JobDefinitionName

[required] The name of the monitoring job definition.

ModelQualityBaselineConfig

Specifies the constraints and baselines for the monitoring job.

ModelQualityAppSpecification

[required] The container that runs the monitoring job.

ModelQualityJobInput

[required] A list of the inputs that are monitored. Currently endpoints are supported.

ModelQualityJobOutputConfig

[required]

JobResources

[required]

NetworkConfig

Specifies the network configuration for the monitoring job.

RoleArn

[required] The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Tags

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Value

A list with the following syntax:

list(
  JobDefinitionArn = "string"
)

Request syntax

svc$create_model_quality_job_definition(
  JobDefinitionName = "string",
  ModelQualityBaselineConfig = list(
    BaseliningJobName = "string",
    ConstraintsResource = list(
      S3Uri = "string"
    )
  ),
  ModelQualityAppSpecification = list(
    ImageUri = "string",
    ContainerEntrypoint = list(
      "string"
    ),
    ContainerArguments = list(
      "string"
    ),
    RecordPreprocessorSourceUri = "string",
    PostAnalyticsProcessorSourceUri = "string",
    ProblemType = "BinaryClassification"|"MulticlassClassification"|"Regression",
    Environment = list(
      "string"
    )
  ),
  ModelQualityJobInput = list(
    EndpointInput = list(
      EndpointName = "string",
      LocalPath = "string",
      S3InputMode = "Pipe"|"File",
      S3DataDistributionType = "FullyReplicated"|"ShardedByS3Key",
      FeaturesAttribute = "string",
      InferenceAttribute = "string",
      ProbabilityAttribute = "string",
      ProbabilityThresholdAttribute = 123.0,
      StartTimeOffset = "string",
      EndTimeOffset = "string",
      ExcludeFeaturesAttribute = "string"
    ),
    BatchTransformInput = list(
      DataCapturedDestinationS3Uri = "string",
      DatasetFormat = list(
        Csv = list(
          Header = TRUE|FALSE
        ),
        Json = list(
          Line = TRUE|FALSE
        ),
        Parquet = list()
      ),
      LocalPath = "string",
      S3InputMode = "Pipe"|"File",
      S3DataDistributionType = "FullyReplicated"|"ShardedByS3Key",
      FeaturesAttribute = "string",
      InferenceAttribute = "string",
      ProbabilityAttribute = "string",
      ProbabilityThresholdAttribute = 123.0,
      StartTimeOffset = "string",
      EndTimeOffset = "string",
      ExcludeFeaturesAttribute = "string"
    ),
    GroundTruthS3Input = list(
      S3Uri = "string"
    )
  ),
  ModelQualityJobOutputConfig = list(
    MonitoringOutputs = list(
      list(
        S3Output = list(
          S3Uri = "string",
          LocalPath = "string",
          S3UploadMode = "Continuous"|"EndOfJob"
        )
      )
    ),
    KmsKeyId = "string"
  ),
  JobResources = list(
    ClusterConfig = list(
      InstanceCount = 123,
      InstanceType = "ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.16xlarge"|"ml.g5.12xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.r5d.large"|"ml.r5d.xlarge"|"ml.r5d.2xlarge"|"ml.r5d.4xlarge"|"ml.r5d.8xlarge"|"ml.r5d.12xlarge"|"ml.r5d.16xlarge"|"ml.r5d.24xlarge",
      VolumeSizeInGB = 123,
      VolumeKmsKeyId = "string"
    )
  ),
  NetworkConfig = list(
    EnableInterContainerTrafficEncryption = TRUE|FALSE,
    EnableNetworkIsolation = TRUE|FALSE,
    VpcConfig = list(
      SecurityGroupIds = list(
        "string"
      ),
      Subnets = list(
        "string"
      )
    )
  ),
  RoleArn = "string",
  StoppingCondition = list(
    MaxRuntimeInSeconds = 123
  ),
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  )
)