DescribeModelPackageResponse

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

A description provided for the model approval.

Whether the model package is certified for listing on Amazon Web Services Marketplace.

Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

A timestamp specifying when the model package was created.

The metadata properties associated with the model package versions.

The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

Details about inference jobs that can be run with models based on this model package.

Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

The last time that the model package was modified.

Metadata properties of the tracking entity, trial, or trial component.

The approval status of the model package.

Metrics for the model.

The Amazon Resource Name (ARN) of the model package.

A brief summary of the model package.

If the model is a versioned model, the name of the model group that the versioned model belongs to.

The name of the model package being described.

The current status of the model package.

Details about the current status of the model package.

The version of the model package.

The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).

Indicates if you want to skip model validation.

Details about the algorithm that was used to create the model package.

The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.

Configurations for one or more transform jobs that SageMaker runs to test the model package.