How Jumio built a real-time feature store on AWS

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If you’re managing a real-time feature store, you might be facing challenges such as data duplication, feature engineering, feature consistency, manual deployment, and latency. Jumio is an identity verification provider that helps businesses detect fraud and build digital trust. To provide these services in real time, Jumio’s machine learning (ML) models needed a real-time feature store that solves these challenges. We use Jumio’s case study to show you how to build a real-time feature store. This architecture pattern applies to ML use cases that require sub-100ms latency for real-time predictions. In this post, we show you the architecture, the design trade-offs, and their impact on Jumio’s workload. You learn how to optimize your ML feature management on AWS by using services such as Amazon SageMaker Feature Store, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Streams. Problem statement Before building the real-time feature store, feature engineering and deployment were often fragmented and inefficient. This led to the following issues: Data duplication: Teams maintained their own offline feature stores, resulting in redundant data and inconsistent feature definitions. Manual production deployment: Teams manually re-implemented features trained offline in production code (Java or Python), which increased the risk of mismatches and bugs. Latency challenges: Fraud detection demands immediate access to features, including upstream model outputs. Delayed event handling: Certain event types arrive with delays or in irregular patterns. They can …

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