Posted On: Feb 2, 2022

AWS Solutions has updated the Maintaining Personalized Experiences with Machine Learning solution, which is an AWS Solutions Implementation that provides end-to-end automation and scheduling for your Amazon Personalize resources. This solution keeps your item and user data current and manages re-training for your models to ensure that recommendations are kept up-to-date with recent user activity and to retain their relevance for your users. This solution publishes Amazon Personalize model offline metrics to Amazon CloudWatch to provide a directional sense of the quality of your models over time.

For users getting started with Amazon Personalize, this release adds support for e-commerce and video-on-demand use cases through Amazon Personalize recommenders. To get started with recommenders, you can now define your dataset group domain, schema domains and recommender configurations. Datasets can be (re)imported on a schedule specified with the solution, and Amazon Personalize will handle model retraining and deployment.

This release also adds support for the creation of user segmentation solutions and batch segment jobs. To get started with user segmentation, users can now use the user segmentation recipes in their configuration, and optionally define batch segmentation jobs and schedules.

To learn more about the Maintaining Personalized Experiences with Machine Learning solution, see the AWS Solutions Implementation webpage.

Additional AWS Solutions are available on the AWS Solutions Implementation webpage, where customers can browse solutions by product category or industry to find AWS-vetted, automated, turnkey reference implementations that address specific business needs.