Real-time ML Feature Store

Built a low-latency feature store powering risk models with millisecond SLAs and versioned features.

GoTypeScriptgRPCBigQueryRedis
Real-time ML Feature Store

Problem

Models starved for fresh features; batch pipelines led to stale predictions and drift.

Approach

Unified offline/online features, introduced stream processors, versioning, and consistency checks.

Architecture

Streaming ingestion (Kafka), Flink processors, Redis as online store, BigQuery as offline, gRPC APIs with typed contracts.

Tradeoffs

Higher infra cost and operational load for strong consistency and low latency guarantees.

Outcome

AUC up 7%, fraud losses down double digits; engineers self-serve features in hours not weeks.