The AWS Machine Learning Blog describes how Jumio built a centralized, real-time feature store on AWS to support fraud detection. The architecture combines Amazon SageMaker Feature Store, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Streams. According to the post, the system delivers sub-100ms feature serving and saves approximately $120,000 annually.
Why it matters
Low-latency feature serving is important for fraud detection, where decisions must be made quickly. Centralizing features can also reduce operational overhead and, in Jumio’s case, deliver measurable cost savings.
Who should care
Machine learning and platform teams evaluating real-time feature infrastructure on AWS may find the architecture and the choice of managed streaming and feature store services relevant to similar use cases.