Creating your product listing

The following is a walkthrough for creating your product listing in the AWS Marketplace for both model package and algorithm products.

Note

Before creating your listing, ensure that you have the required resources specified in Requirements and best practices for creating machine learning products.

The process has the following steps:

Step 1: Create a new listing

To get started with a machine learning product, you'll initiate the listing process by setting the product name, adding optional resource tags for organization, and generating the product ID. The product ID is used to track your product throughout its lifecycle.

1. Sign in to your seller AWS account and go to the AWS Marketplace Management Portal.

2. In the top menu, go to Products and then choose Machine learning.

3. Choose Create machine learning product.

4. Under Product name, enter a unique product name that will be displayed to buyers at the top of the product listing page and in search results.

5. (Optional) Under Tags, enter any tags you want to associate with the product. For more information, see Tagging AWS resources.

6. Under Product ID and code, choose Generate product ID and code.

7. Choose Continue to wizard. You'll start the process of adding detailed product information in the wizard.

Step 2: Provide product information

When listing your machine learning product in AWS Marketplace, providing comprehensive and accurate product information is crucial. Use the Provide product information step in the wizard to capture essential details about your offering such as product categories and support information.

1. Enter information about your product.

2. Choose Next to move to the next step in the wizard.

Step 3: Add initial product version

This page guides you through adding the initial version of your product. Your product may have multiple versions throughout its lifecycle, and each version is identified by a unique SageMaker AI ARN.

1. Under Amazon Resource Names (ARNs), enter the Amazon SageMaker AI ARN and IAM access role ARN (if applicable). You can find your Amazon SageMaker AI ARN in the SageMaker AI console: https://console.aws.amazon.com/sagemaker/.

   Example model package ARN: arn:aws:sagemaker:<region>:<account-id>:model-package/<model-package-name>

   Example algorithm ARN: arn:aws:sagemaker:<region>:<account-id>:algorithm/<algorithm-name>

   Example IAM ARN: arn:aws:iam::<account-id>:role/<role-name>

2. Under Version information, enter a Version name and Release notes..

3. Under Model input details, enter a summary of the model inputs and provide sample input data for real-time and batch job inputs. Optionally, you can provide any input limitations.

4. (Optional) Under Input parameters, provide detailed information about each input parameter supported by your product. You can provide the parameter name, a description, constraints, and specify if the parameter is required or optional. You can provide up to 24 input parameters.

5. (Optional) Under Custom attributes, provide any custom invocation parameters supported by your product. For each attribute, you can provide a name, description, constraints, and specify if the attribute is required or optional.

6. Under Model output details, enter a summary of the model outputs and provide sample output data for real-time and batch job outputs. Optionally, you can provide any output limitations.

7. (Optional) Under Output parameters, provide detailed information about each output parameter supported by your product. You can provide the parameter name, a description, constraints, and specify if the parameter is required or optional. You can provide up to 24 output parameters.

8. Under Usage instructions, provide clear instructions for using your model effectively such as best practices, how to handle common edge cases, or performance optimization suggestions.

9. Under Git repository and notebook links, provide links to example notebooks and Git repository. Sample notebooks should include how to invoke your model. Your Git repository should include notebooks, data files, and other developer tools.

10. Under Recommended instance types, select the recommended instance types for your product.

    For model packages, you'll select recommended instance types for both batch transform and real-time inference.

    For algorithm packages, you'll select the recommended instance type for training jobs.

    Note

    The instance types available to select are limited to those supported by your model or algorithm package. These supported instance types were determined when you initially created your resources in Amazon SageMaker AI. This ensures that your product is only associated with hardware configurations that can effectively run your machine learning solution.

11. Choose Next to move to the next step in the wizard.

Step 4: Configure the pricing model

When configuring your product's pricing model, you can offer your product for free or implement usage-based pricing. Your pricing model cannot be changed after you've published the product.

1. Choose a pricing model. Batch transform and algorithm training products can only be free or charged for hourly usage.

   • If you chose to offer your product for free, choose Next and continue the wizard.

   • If you chose usage pricing, continue these steps.

2. If you chose to charge based on usage, you can enter usage costs. You can choose to enter a price that applies to all instance types or enter a price per instance type for more granular pricing.

3. Select Yes, offer a free trial if you'd like to offer a free trial of your product.

4. Choose Next to move to the next step in the wizard.

Step 5: Configure refund policy

Though you're not required to offer refunds, you must file an official refund policy with AWS Marketplace.

1. Enter a refund policy.

2. Choose Next to move to the next step in the wizard.

Step 6: Configure EULA

In this step, you'll choose the legal agreement that will govern how customers can use your product. You can either select AWS's standard contract terms or upload your own custom end-user license agreement (EULA).

1. Select either the standard contract or provide a custom end-user license agreement.

2. Choose Next to move to the next step in the wizard.

Step 7: Configure allowlist

Before submitting your product, you'll need to specify which AWS accounts can access it. This optional step controls the initial visibility of your product, limiting access to your own account and any specifically authorized AWS accounts you add to the allowlist.

1. Enter the AWS account IDs you want to access your product.

2. Choose Submit to submit your product.

   Your product will have the Limited visibility status and will only be visible to the AWS account that created the product and other allow-listed AWS accounts.

   For more information on statuses, see Machine learning product status.

You can view and test your product listing while it's in Limited visibility. When you're ready to change the visibility of your product, see Updating product visibility.