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Use a Model Package to Create a Model

Use a model package to create a deployable model that you can use to get real-time inferences by creating a hosted endpoint or to run batch transform jobs. You can create a deployable model from a model package by using the Amazon SageMaker console, the low-level Amazon SageMaker API), or the Amazon SageMaker Python SDK.

Topics + Use a Model Package to Create a Model (Console) + Use a Model Package to Create a Model (API) + Use a Model Package to Create a Model (Amazon SageMaker Python SDK)

To create a deployable model from a model package (console)

  1. Open the Amazon SageMaker console at https://console.aws.amazon.com/sagemaker/.

  2. Choose Model packages.

  3. Choose a model package that you created from the list on the My model packages tab or choose a model package that you subscribed to on the AWS Marketplace subscriptions tab.

  4. Choose Create model.

  5. For Model name, type a name for the model.

  6. For IAM role, choose an IAM role that has the required permissions to call other services on your behalf, or choose Create a new role to allow Amazon SageMaker to create a role that has the AmazonSageMakerFullAccess managed policy attached. For information, see Amazon SageMaker Roles.

  7. For VPC, choose a Amazon VPC that you want to allow the model to access. For more information, see Give Amazon SageMaker Hosted Endpoints Access to Resources in Your Amazon VPC.

  8. Leave the default values for Container input options and Choose model package.

  9. For environment variables, provide the names and values of environment variables you want to pass to the model container.

  10. For Tags, specify one or more tags to manage the model. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.

  11. Choose Create model.

After you create a deployable model, you can use it to set up an endpoint for real-time inference or create a batch transform job to get inferences on entire datasets. For information about hosted endpoints in Amazon SageMaker, see Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services. For information about batch transform jobs, see Step 6.2: Deploy the Model with Batch Transform.

To use a model package to create a deployable model by using the Amazon SageMaker API, specify the name or the Amazon Resource Name (ARN) of the model package as the ModelPackageName field of the ContainerDefinition object that you pass to the CreateModel API.

After you create a deployable model, you can use it to set up an endpoint for real-time inference or create a batch transform job to get inferences on entire datasets. For information about hosted endpoints in Amazon SageMaker, see Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services. For information about batch transform jobs, see Step 6.2: Deploy the Model with Batch Transform.

To use a model package to create a deployable model by using the Amazon SageMaker Python SDK, initialize a ModelPackage object, and pass the Amazon Resource Name (ARN) of the model package as the model_package_arn argument. For example:

from sagemaker import ModelPackage
model = ModelPackage(role='SageMakerRole',
                     model_package_arn='training-job-scikit-decision-trees-1542660466-6f92',
                     sagemaker_session=sagemaker_session)

After you create a deployable model, you can use it to set up an endpoint for real-time inference or create a batch transform job to get inferences on entire datasets. For information about hosted endpoints in Amazon SageMaker, see Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services. For information about batch transform jobs, see Step 6.2: Deploy the Model with Batch Transform.