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Developers building production ML systems can adopt this open architecture pattern to reduce feature serving latency and operational costs, while business stakeholders evaluating ML infrastructure investments gain concrete evidence of AWS tooling ROI in fraud detection workloads.
Jumio implemented a real-time feature store on AWS using SageMaker Feature Store, Managed Flink, and Kinesis Data Streams to support fraud detection with sub-100ms latency. The architecture delivers approximately $120,000 in annual cost savings and serves as a reference implementation for ML infrastructure at scale.
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CoFabrix summarises and comments on this story. The original reporting belongs to AWS AI/ML.
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