How Jumio built a real-time feature store on AWS

The ability to centralize and deliver data features in milliseconds can fundamentally transform how businesses respond to real-time events, from fraud prevention to personalized customer experiences. This AWS Machine Learning piece details how Jumio architected a real-time feature store using Amazon SageMaker Feature Store, Apache Flink, and Kinesis Data Streams. The core achievement is a highly available system that serves machine learning features in under 100 milliseconds for critical fraud detection, while simultaneously yielding significant operational cost savings. For a mid-sized e-commerce platform based in Seattle, Washington, this approach could mean instantly flagging suspicious transactions before they complete, drastically reducing chargebacks and manual review overhead. Imagine a logistics startup in Dallas, Texas, tracking thousands of deliveries daily; they could leverage such a feature store to feed real-time traffic, weather, and driver availability data into dynamic routing models, optimizing delivery times and fuel consumption on the fly. An independent SaaS founder in Denver, Colorado, building a tool for financial planning could use this architecture to power a highly responsive recommendation engine, offering tailored investment advice or credit alerts based on a user's up-to-the-minute financial activities and market shifts. The practical impact extends beyond just saving money, enabling entirely new categories of responsive, data-driven applications. To begin exploring this concept yourself, identify one critical, time-sensitive decision point in your current system that could benefit from real-time data input—perhaps personalizing a user experience based on their last three interactions, or rapidly assessing risk for a transaction. Then, sketch out how you might collect and serve just one or two key data points for that specific decision within a target latency of under one second.