sagemaker_create_model_explainability_job_definition: Creates the definition for a model explainability job

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

Creates the definition for a model explainability job

Description

Creates the definition for a model explainability job.

See https://www.paws-r-sdk.com/docs/sagemaker_create_model_explainability_job_definition/ for full documentation.

Usage

sagemaker_create_model_explainability_job_definition(
  JobDefinitionName,
  ModelExplainabilityBaselineConfig = NULL,
  ModelExplainabilityAppSpecification,
  ModelExplainabilityJobInput,
  ModelExplainabilityJobOutputConfig,
  JobResources,
  NetworkConfig = NULL,
  RoleArn,
  StoppingCondition = NULL,
  Tags = NULL
)

Arguments

JobDefinitionName	[required] The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
ModelExplainabilityBaselineConfig	The baseline configuration for a model explainability job.
ModelExplainabilityAppSpecification	[required] Configures the model explainability job to run a specified Docker container image.
ModelExplainabilityJobInput	[required] Inputs for the model explainability job.
ModelExplainabilityJobOutputConfig	[required]
JobResources	[required]
NetworkConfig	Networking options for a model explainability job.
RoleArn	[required] The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
StoppingCondition
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.

paws.machine.learning documentation built on Sept. 12, 2024, 6:23 a.m.