From Python to Go: Engineering a 71K RPS Fraud Detection Pipeline

This technical deep dive reveals how to build production ML inference systems at extreme scale. By migrating XGBoost models to ONNX and serving them through Go instead of Python, engineers can achieve 71,000 requests per second with sub-6ms latency while maintaining high accuracy (86% recall, 74% precision). The pattern—combining model format standardization, compiled language runtimes, and strategic caching—is directly applicable to any organization deploying real-time ML systems under strict latency constraints.