A Databricks engineering post walks through a sample real-time fraud-detection system that scores credit-card transactions in under 40 milliseconds using Model Serving alongside Lakebase, its managed Postgres offering. The pipeline retrieves customer features from the database, feeds them to a CatBoost model deployed through MLflow to produce a fraud-probability score, and then applies business rules, using connection pooling, OAuth token rotation, and database autoscaling to hold single-digit-millisecond lookup latencies. The piece is framed as a reference architecture for low-latency ML inference on transactional data.
