How Jumio built a real-time feature store on AWS | Amazon Web Services

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...

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