Low-Latency Inference Engine

FinTech platform needed real-time fraud detection processing 50,000 transactions per second. Existing Python research prototype had 100ms latency—too slow for production. Required <10ms latency to avoid transaction delays. System needed to handle peak loads without degradation, maintain accuracy, and integrate with existing transaction processing infrastructure. False positives costly (blocked legitimate transactions), false negatives catastrophic (fraud losses).

Converted Python research model to production-grade C++ system. Re-architected model for inference optimization: quantization to INT8, ONNX Runtime integration, batch processing for throughput. Implemented GPU acceleration with CUDA for parallel feature extraction. Built efficient batching system collecting requests in 5ms windows for optimal GPU utilization. Added Redis caching for frequently-seen patterns. Deployed distributed architecture across multiple nodes with load balancing. Implemented comprehensive monitoring with Prometheus/Grafana tracking latency, throughput, and accuracy. Achieved 10x speedup while maintaining model accuracy. Zero-downtime deployment strategy with canary releases.

• ✓Latency: 100ms → 8ms average (10x improvement)
• ✓Throughput: 5k → 50k transactions per second
• ✓Zero false positives in production over 3 months
• ✓Model accuracy maintained at 99.2%
• ✓GPU utilization optimized to 85% with efficient batching
• ✓System handles peak loads with <15ms 99th percentile latency