Posted On: Oct 9, 2020

We are excited to announce efficiency improvements for Amazon Personalize that decrease the time required to train models by up to 40% and reduce the latency for generating real-time recommendations by up to 30%. Amazon Personalize enables developers to build applications with the same machine learning (ML) technology used by for real-time personalized recommendations – no ML expertise required. Amazon Personalize provisions the necessary infrastructure and manages the entire ML pipeline, including processing the data, identifying features, using the best algorithms, and training, optimizing, and hosting the models.

To use Amazon Personalize, you need to provide the service user interaction data (page views, sign-ups, purchases, etc.) from your applications, along with optional user demographic information (age, location, etc.) and inventory of the items you want to recommend, such as articles, products, videos, or music. Then, entirely under the covers, Amazon Personalize will process and examine the data, identify what is meaningful, trains and optimizes a personalization model that is customized for your data, and is accessible via an API that can be easily invoked by your business application. You can use both historical data stored in Amazon Simple Storage Service (S3) and streaming data sent in real-time from a JavaScript tracker or server-side.

To see all the regions Amazon Personalize is available in, visit the AWS Region page. Get started with Amazon Personalize by visiting the console and documentation.