Category: HAQM Personalize

The HAQM Web Services (AWS) Summit 2025 season launched this week, starting with the Paris Summit. These free events bring together the global cloud computing community for learning and collaboration. AWS Community Day Romania, held on April 11th, showcased how the local community creates opportunities for collective growth and inclusion. Last week’s launches Announcing up […]

Today, we celebrate Martin Luther King Jr. Day in the US to honor the late civil rights leader’s life, legacy, and achievements. In this article, HAQM employees share what MLK Day means to them and how diversity makes us stronger. Coming back to our AWS Week in Review—it’s been a busy week! Last Week’s Launches […]

I love the AWS Twitch channel for watching interesting online live shows such as AWS On Air, Containers from the Couch, and Serverless Office Hours. Last week, AWS Storage Day 2022 was hosted virtually on the AWS Twitch channel and covered recent announcements and insights that address customers’ needs to reduce and optimize storage costs and build data resiliency […]

Today, we’re very happy to announce that HAQM Personalize now supports batch recommendations. Launched at AWS re:Invent 2018, Personalize is a fully-managed service that allows you to create private, customized recommendations for your applications, with little to no machine learning experience required. With Personalize, you provide the unique signals in your activity data (page views, […]

Today, we’re happy to announce that HAQM Personalize is available to all AWS customers. Announced in preview at AWS re:Invent 2018, HAQM Personalize is a fully-managed service that allows you to create private, customized personalization recommendations for your applications, with little to no machine learning experience required. Whether it is a timely video recommendation inside […]

Machine learning definitely offers a wide range of exciting topics to work on, but there’s nothing quite like personalization and recommendation. At first glance, matching users to items that they may like sounds like a simple problem. However, the task of developing an efficient recommender system is challenging. Years ago, Netflix even ran a movie […]