Values_2.ModelPackageSourceA container for your trained model that can be deployed for SageMaker inference. This can include inference code, artifacts, and metadata. The model package type can be one of the following. Versioned model: A part of a model package group in Model Registry. Unversioned model: Not part of a model package group and used in Amazon Web Services Marketplace. For more information, see CreateModelPackage .
type nonrec t = {modelPackageName : Values_0.EntityName.t option;The name of the model package. The name can be as follows: For a versioned model, the name is automatically generated by SageMaker Model Registry and follows the format 'ModelPackageGroupName/ModelPackageVersion'. For an unversioned model, you must provide the name.
*)modelPackageGroupName : Values_0.EntityName.t option;The model group to which the model belongs.
*)modelPackageVersion : Values_0.ModelPackageVersion.t option;The version number of a versioned model.
*)modelPackageRegistrationType : Values_0.ModelPackageRegistrationType.t option;The package registration type of the model package.
*)modelPackageArn : Values_0.ModelPackageArn.t option;The Amazon Resource Name (ARN) of the model package.
*)modelPackageDescription : Values_0.EntityDescription.t option;The description of the model package.
*)creationTime : Values_0.CreationTime.t option;The time that the model package was created.
*)inferenceSpecification : Values_0.InferenceSpecification.t option;Defines how to perform inference generation after a training job is run.
*)sourceAlgorithmSpecification : Values_1.SourceAlgorithmSpecification.t option;A list of algorithms that were used to create a model package.
*)validationSpecification : Values_1.ModelPackageValidationSpecification.t option;Specifies batch transform jobs that SageMaker runs to validate your model package.
*)modelPackageStatus : Values_0.ModelPackageStatus.t option;The status of the model package. This can be one of the following values. PENDING - The model package is pending being created. IN_PROGRESS - The model package is in the process of being created. COMPLETED - The model package was successfully created. FAILED - The model package failed. DELETING - The model package is in the process of being deleted.
*)modelPackageStatusDetails : Values_1.ModelPackageStatusDetails.t option;Specifies the validation and image scan statuses of the model package.
*)certifyForMarketplace : Values_0.CertifyForMarketplace.t option;Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace.
*)modelApprovalStatus : Values_0.ModelApprovalStatus.t option;The approval status of the model. This can be one of the following values. APPROVED - The model is approved REJECTED - The model is rejected. PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.
*)createdBy : Values_0.UserContext.t option;Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
*)metadataProperties : Values_0.MetadataProperties.t option;Metadata properties of the tracking entity, trial, or trial component.
*)modelMetrics : Values_1.ModelMetrics.t option;Metrics for the model.
*)lastModifiedTime : Values_0.Timestamp.t option;The last time the model package was modified.
*)lastModifiedBy : Values_0.UserContext.t option;Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
*)approvalDescription : Values_0.ApprovalDescription.t option;A description provided when the model approval is set.
*)domain : Values_0.String_.t option;The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
*)task : Values_0.String_.t option;The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
*)samplePayloadUrl : Values_0.String_.t option;The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
*)additionalInferenceSpecifications : Values_0.AdditionalInferenceSpecifications.t
option;An array of additional Inference Specification objects.
*)sourceUri : Values_1.ModelPackageSourceUri.t option;The URI of the source for the model package.
*)securityConfig : Values_1.ModelPackageSecurityConfig.t option;modelCard : Values_1.ModelPackageModelCard.t option;modelLifeCycle : Values_1.ModelLifeCycle.t option;A structure describing the current state of the model in its life cycle.
*)customerMetadataProperties : Values_1.CustomerMetadataMap.t option;The metadata properties for the model package.
*)driftCheckBaselines : Values_1.DriftCheckBaselines.t option;Represents the drift check baselines that can be used when the model monitor is set using the model package.
*)skipModelValidation : Values_1.SkipModelValidation.t option;Indicates if you want to skip model validation.
*)}val make :
?modelPackageName:??? ->
?modelPackageGroupName:??? ->
?modelPackageVersion:??? ->
?modelPackageRegistrationType:??? ->
?modelPackageArn:??? ->
?modelPackageDescription:??? ->
?creationTime:??? ->
?inferenceSpecification:??? ->
?sourceAlgorithmSpecification:??? ->
?validationSpecification:??? ->
?modelPackageStatus:??? ->
?modelPackageStatusDetails:??? ->
?certifyForMarketplace:??? ->
?modelApprovalStatus:??? ->
?createdBy:??? ->
?metadataProperties:??? ->
?modelMetrics:??? ->
?lastModifiedTime:??? ->
?lastModifiedBy:??? ->
?approvalDescription:??? ->
?domain:??? ->
?task:??? ->
?samplePayloadUrl:??? ->
?additionalInferenceSpecifications:??? ->
?sourceUri:??? ->
?securityConfig:??? ->
?modelCard:??? ->
?modelLifeCycle:??? ->
?tags:??? ->
?customerMetadataProperties:??? ->
?driftCheckBaselines:??? ->
?skipModelValidation:??? ->
unit ->
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