Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, detecting fraudulent transactions or even dynamically pricing products and services. Real-time machine learning can substantially enhance your customers' experience, resulting in better engagement and retention. In this session, learn how to use AWS data streaming platforms such as Amazon Kinesis to collect and process data in real-time, and Amazon SageMaker Feature Store, which provides a fully managed central repository for ML features, to support real-time machine learning. We will also walk through the architecture that supports the ML-backed decisions in near-real time for a credit card fraud detection use case.
Aneesh Chandra PN, Senior Analytics Specialist Solutions Architect, AWS