Requirements and best practices for creating machine learning products - AWS Marketplace
Required assetsGeneral best practices for ML productsRequirements for usage informationRequirements for inputs and outputsRequirements for Jupyter notebookSummary of requirements and recommendations for ML product listings

Requirements and best practices for creating machine learning products

It is important that your buyers find it easy to test your model package and algorithm products. The following sections describe the requirements for creating machine learning (ML) product listings and best practices for ML products. For a complete summary of requirements and recommendations, see the Summary of requirements and recommendations for ML product listings.

Note

An AWS Marketplace representative might contact you to help you meet these requirements if your published products don't meet them.

Required assets

Before creating a machine learning product listing, ensure that you have the following required assets:

General best practices for ML products

Provide the following information for your machine learning product:

  • For product descriptions, include the following:

    • What your model does

    • Who the target customer is

    • What the most important use case is

    • How your model was trained or the amount of data that was used

    • What the performance metrics are and the validation data used

    • If medical, whether or not your model is for diagnostic use

  • By default, machine learning products are configured to have public visibility. However, you can create a product with private visibility. For more information, see Creating your product listing.

  • (Optional) For paid products, offer a free trial of 14–30 days for customers to try your product. For more information, see Machine learning product pricing for AWS Marketplace.

  • (Optional) For model package products, if you want to enable a real-time product demo on your product listing page, contact the AWS Marketplace Seller Operations team. The product demo allows a prospective buyer to try your model directly on the listing page without subscribing to or deploying the model themselves.

Requirements for usage information

Clear usage information that describes the expected inputs and outputs of your product (with examples) is crucial for driving a positive buyer experience.

With each new version of your resource that you add to your product listing, you must provide usage information.

To add usage information for a new product that you are publishing for the first time, sign into the AWS Marketplace Management Portal console. From the Products dropdown, choose Machine learning. Select your product. In the Product Overview under Launch option, provide the ARN of your model package or algorithm resource, and choose Add.

To edit the existing usage information for a specific version, choose Edit under Launch option and then Edit version.

Requirements for inputs and outputs

A clear explanation of your format, with examples of inputs and outputs, is important to help your buyers to understand and use your product. This understanding helps your buyers to perform any necessary transformations on the input data to get the best inference results.

You will be prompted for the following when adding your Amazon SageMaker resource to your product listing.

Inference inputs and outputs

For inference input, provide the input format for both the real-time endpoint and batch transform job. Include code snippets for any necessary preprocessing of the data. Include supported MIME content types (for example, image/jpeg, image/png, image/bmp), descriptions of values if applicable, and limitations. Include input samples hosted on GitHub.

For inference output, provide the output format for the both real-time endpoint and batch transform job. Include output MIME content type (for example, application/json, image/jpeg) and description of values if applicable. Include output samples hosted on GitHub.

For samples, provide input files that work with your product. If your model performs multiclass classification, provide at least one sample input file for each class.

Training inputs

In the Information to train a model section, provide the input data format and code snippets for any necessary preprocessing of the data. Include supported MIME content types (for example, image/jpeg, image/png, image/bmp), description of values if applicable, and limitations. Ensure to include input samples hosted on GitHub.

Explain both optional and mandatory features that can be provided by the buyer, and specify whether the PIPE input mode is supported. If distributed training (training with more than 1 CPU/GPU instance) is supported, specify this. For tuning, list the recommend hyperparameters.

Requirements for Jupyter notebook

When adding your SageMaker resource to your product listing, provide a link to a sample Jupyter notebook hosted on GitHub that demonstrates the complete workflow without asking the buyer to upload or find any data.

Use the AWS SDK for Python (Boto). A well-developed sample notebook makes it easier for buyers to try and use your listing.

For model package products, your sample notebook demonstrates the preparation of input data, creation of an endpoint for real-time inference, and performance of batch-transform jobs. For more information, see Model Package listing and Sample notebook on GitHub. For a sample notebook, see auto_insurance. The notebook works in all AWS Regions, without entering any parameters and without a buyer needing to locate sample data.

Note

An underdeveloped sample Jupyter notebook that does not show multiple possible inputs and data preprocessing steps might make it difficult for the buyer to fully understand your product's value proposition.

For algorithm products, the sample notebook demonstrates complete training, tuning, model creation, the creation of an endpoint for real-time inference, and the performance of batch-transform jobs. For more information, see Algorithm listing and Sample notebook on GitHub. For sample notebooks, see amazon_demo_product and automl on GitHub. These sample notebooks work in all Regions without entering any parameters and without a buyer needing to locate sample data.

Note

A lack of example training data might prevent your buyer from running the Jupyter notebook successfully. An underdeveloped sample notebook might prevent your buyers from using your product and hinder adoption.

Summary of requirements and recommendations for ML product listings

The following table provides a summary of the requirements and recommendations for a machine learning product listing page.

Details For model package listings For algorithm listings
Product descriptions
Explain in detail what the product does for supported content types (for example, “detects X in images"). Required Required
Provide compelling and differentiating information about the product (avoid adjectives like "best" or unsubstantiated claims). Recommended Recommended
List most important use case(s) for this product. Required Required
Describe the data (source and size) it was trained on and list any known limitations. Required Not applicable
Describe the core framework that the model was built on. Recommended Recommended
Summarize model performance metric on validation data (for example, "XX.YY percent accuracy benchmarked using the Z dataset"). Required Not applicable
Summarize model latency and/or throughput metrics on recommended instance type. Required Not applicable
Describe the algorithm category. For example, “This decision forest regression algorithm is based on an ensemble of tree-structured classifiers that are built using the general technique of bootstrap aggregation and a random choice of features.” Not applicable Required
Usage information
For inference, provide the input format for both the real-time endpoint and batch transform job. Include supported MIME content types (for example, image/jpeg, image/png, image/bmp), description of values if applicable, and limitations. See Requirements for inputs and outputs. Required Required
For inference, provide input samples for both the real-time endpoint and batch transform job. Samples must be hosted on GitHub. See Requirements for inputs and outputs. Required Required
For inference, provide the output format for both the real-time endpoint and batch transform job. Include output MIME content type (for example, application/json, image/jpeg) and description of values if applicable. See Requirements for inputs and outputs. Required Required
For inference, provide output samples for both the real-time endpoint and batch transform job. Samples must be hosted on GitHub. See Requirements for inputs and outputs. Required Required
For inference, provide an example of using an endpoint or batch transform job. Include a code example using the AWS Command Line Interface (AWS CLI) commands or using an AWS SDK. Required Required
For training, provide input format. Include supported MIME content types (for example, image/jpeg, image/png, image/bmp), description of values if applicable, and limitations (for example, minimum rows of data required). See Requirements for inputs and outputs. Not applicable Required
For training, provide input samples hosted on GitHub. See Requirements for inputs and outputs. Not applicable Required
For training, provide an example of performing training jobs. Describe the supported hyperparameters, their ranges, and their overall impact. Specify if the algorithm supports hyperparameter tuning, distributed training, or GPU instances. Include code example such as AWS CLI commands or using an AWS SDK, for example. Not applicable Required
Provide a Jupyter notebook hosted on GitHub demonstrating complete use of your product. See Requirements for Jupyter notebook. Required Required
Provide technical information related to the usage of the product, including user manuals and sample data. Recommended Recommended