SageMaker Model Explainability Job Definition

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An AWS SageMaker Model Explainability Job Definition is a resource that specifies the configuration for running explainability jobs on machine learning models. It defines how to generate insights into model predictions, such as feature importance or bias detection, using preconfigured tools. This job definition includes details like the model to analyze, input datasets, output locations, and compute resources. It allows you to consistently run explainability analyses on models to improve transparency, compliance, and trust in machine learning outcomes.

aws.sagemaker_model_explainability_job_definition

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
creation_time	core	timestamp	The time at which the model explainability job was created.
job_definition_arn	core	string	The Amazon Resource Name (ARN) of the model explainability job.
job_definition_name	core	string	The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
job_resources	core	json	Identifies the resources to deploy for a monitoring job.
model_explainability_app_specification	core	json	Configures the model explainability job to run a specified Docker container image.
model_explainability_baseline_config	core	json	The baseline configuration for a model explainability job.
model_explainability_job_input	core	json	Inputs for the model explainability job.
model_explainability_job_output_config	core	json	The output configuration for monitoring jobs.
network_config	core	json	Networking options for a model explainability job.
role_arn	core	string	The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
stopping_condition	core	json	A time limit for how long the monitoring job is allowed to run before stopping.
tags	core	hstore