See: newCreateModelPackage smart constructor.

Create a value of CreateModelPackage with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

CreateModelPackage, createModelPackage_additionalInferenceSpecifications - 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.

CreateModelPackage, createModelPackage_certifyForMarketplace - Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

$sel:clientToken:CreateModelPackage', createModelPackage_clientToken - A unique token that guarantees that the call to this API is idempotent.

CreateModelPackage, createModelPackage_customerMetadataProperties - The metadata properties associated with the model package versions.

CreateModelPackage, createModelPackage_domain - The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

CreateModelPackage, createModelPackage_driftCheckBaselines - 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.

CreateModelPackage, createModelPackage_inferenceSpecification - Specifies details about inference jobs that can be run with models based on this model package, including the following:

• The Amazon ECR paths of containers that contain the inference code and model artifacts.
• The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
• The input and output content formats that the model package supports for inference.

CreateModelPackage, createModelPackage_metadataProperties - Undocumented member.

CreateModelPackage, createModelPackage_modelApprovalStatus - Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

CreateModelPackage, createModelPackage_modelMetrics - A structure that contains model metrics reports.

CreateModelPackage, createModelPackage_modelPackageDescription - A description of the model package.

CreateModelPackage, createModelPackage_modelPackageGroupName - The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

CreateModelPackage, createModelPackage_modelPackageName - The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

CreateModelPackage, createModelPackage_samplePayloadUrl - The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

CreateModelPackage, createModelPackage_sourceAlgorithmSpecification - Details about the algorithm that was used to create the model package.

CreateModelPackage, createModelPackage_tags - A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

CreateModelPackage, createModelPackage_task - The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".

Specify "OTHER" if none of the tasks listed fit your use case.

CreateModelPackage, createModelPackage_validationSpecification - Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.

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.

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

A unique token that guarantees that the call to this API is idempotent.

The metadata properties associated with the model package versions.

The machine learning domain of your model package and its components. 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.

Specifies details about inference jobs that can be run with models based on this model package, including the following:

• The Amazon ECR paths of containers that contain the inference code and model artifacts.
• The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
• The input and output content formats that the model package supports for inference.

Undocumented member.

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

A structure that contains model metrics reports.

A description of the model package.

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

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

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

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".

Specify "OTHER" if none of the tasks listed fit your use case.

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

See: newCreateModelPackageResponse smart constructor.

Create a value of CreateModelPackageResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:httpStatus:CreateModelPackageResponse', createModelPackageResponse_httpStatus - The response's http status code.

CreateModelPackageResponse, createModelPackageResponse_modelPackageArn - The Amazon Resource Name (ARN) of the new model package.

The response's http status code.

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