Machine learning products - AWS Marketplace

Machine learning products

AWS Marketplace has a category for machine learning products you can subscribe to through AWS Marketplace. The product category is Machine Learning. The products in this category include machine learning (ML) algorithms and model packages.

An Amazon SageMaker algorithm is a unique Amazon SageMaker entity that is identified by an Amazon Resource Name (ARN). An algorithm has two logical components: training and inference. Customers use the training component to create a training job or tuning job using your input dataset in Amazon SageMaker to build machine learning models. Amazon SageMaker saves the model artifacts generated by the algorithm during training to an Amazon Simple Storage Service (Amazon S3) bucket. Customers can build a model package using the algorithm’s inference component and the model artifacts that are stored in the S3 bucket. Customers can use this model package to build a model, which can then be used for running on hosting services or running batch transforms in Amazon SageMaker.

An Amazon SageMaker model package is a unique pretrained ML model that is identified by an ARN on Amazon SageMaker. Customers use a model package to create a model in Amazon SageMaker. Then, the model can be used with hosting services to run real-time inference or with batch transform to run batch inference in Amazon SageMaker.

You can browse and search for hundreds of ML algorithms and model packages from a broad range of subcategories, such as computer vision, natural language processing, speech recognition, text, data, voice, image, video analysis, fraud detection, and predictive analysis.

To assess the quality and suitability of a model, you can review product descriptions, usage instructions, customer reviews, sample Jupyter notebooks, pricing, and support information. You deploy models directly from the Amazon SageMaker console, through a Jupyter notebook, with the Amazon SageMaker SDK, or using the AWS Command Line Interface AWS CLI. Amazon SageMaker provides a secure environment to run your training and inference jobs by running a static scan on all marketplace products.

Find, subscribe, and deploy

To find, buy and deploy machine learning products, you find and subscribe to products on AWS Marketplaceand then deploy the product on Amazon SageMaker.

You pay only for your usage, with no minimum fees or upfront commitments. AWS Marketplace provides a consolidated bill for algorithms and model packages, and AWS infrastructure usage charges. To find, subscribe, and deploy Amazon SageMaker algorithms and model packages:

  1. From the AWS Marketplace website, under Find AWS Marketplace products that meet your needs, use the Categories drop-down menu to find the subcategory under Machine Learning that you are interested in. You can refine your search results by applying resource type, category, and pricing filters. From search results, you can access the product detail page, which allows you to review the product description, usage instructions, customer reviews, data requirements, sample Jupyter notebooks, and pricing and support information.

  2. To view the procurement page, from the product detail page, choose Continue to subscribe. After reviewing the product pricing information and the end user license agreement (EULA), you can subscribe. After subscribing, you can configure the product (for example, by selecting a specific version or deployment region) on the AWS Marketplace website.

  3. After configuring the product, you can view the Amazon SageMaker product detail page by choosing View in Amazon SageMaker. From the Amazon SageMaker console, you can deploy the algorithms and model packages using the Amazon SageMaker console, Jupyter notebook, Amazon SageMaker CLI commands, or API operations.

To deploy a third-party algorithm/model package on Amazon SageMaker, you need a valid subscription. Find the suitable algorithm/model package from AWS Marketplace and then subscribe to the products. Navigate to Your Marketplace Software and make sure that you have a valid subscription to the algorithm you want to deploy.

For more information about deploying on Amazon SageMaker, see Getting Started.