Jumio Builds Real-Time Feature Store on AWS
Key takeaways
- Jumio built a real-time feature store on AWS.
- Key services include SageMaker Feature Store, Flink, and Kinesis.
- The solution achieves sub-100ms latency for fraud detection.
- It resulted in significant annual cost savings.
Who benefits
Summary
Jumio developed a centralized, real-time feature store on AWS using Amazon SageMaker Feature Store, Apache Flink, and Kinesis Data Streams. This architecture delivers sub-100ms feature serving for fraud detection, saving approximately $120,000 annually.
Why it matters
Real-time feature stores are crucial for high-performance machine learning applications like fraud detection, enabling faster, more accurate decisions and significant cost efficiencies.
How to implement this in your domain
- 1Assess current data pipelines and identify features critical for real-time ML models.
- 2Design a feature store architecture leveraging AWS services like SageMaker Feature Store, Kinesis, and Flink.
- 3Migrate or build data ingestion pipelines to populate the feature store in real-time.
- 4Integrate the real-time feature store with existing or new machine learning models for inference.
- 5Monitor performance and cost, optimizing the architecture for efficiency and latency.
Original post by Amit Peshwani
"Learn how Jumio built a centralized, real-time feature store on AWS with Amazon SageMaker Feature Store, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Streams. The architecture delivers sub-100ms feature serving for fraud detection and saves approximately $120,…"
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