Databricks has shared a practical walkthrough of what a real-time payment decision can look like on the platform, combining a Databricks App, Model Serving with route optimization, and Lakebase to score transactions quickly while keeping the data path and inference path on the same governed foundation.
What’s new
- A full app pattern for real-time ML: The sample app uses a FastAPI backend, a React frontend, Databricks Apps, and Lakebase as the online feature and serving store for fraud scoring.
- Faster inference with route optimization: Route-optimized serving improves the network path for inference requests, helping lower overhead latency and support higher throughput for interactive ML workloads.
- Lakebase for low-latency operational reads: Lakebase is positioned as a fully managed Postgres database in Databricks that can support real-time application reads, online ML features, and operational state alongside lakehouse data.
- Built for live application demand: Lakebase autoscaling adjusts compute as workload demand changes, and can work with scale-to-zero to reduce cost during idle periods.
- Strong fit for fraud and other interactive ML use cases: Databricks highlights high-QPS, low-latency Model Serving for workloads like fraud detection, recommendations, and search, where consistent response times matter.
This walkthrough shows how application design, serving optimization, operational data access, and autoscaling all work together to support real-time ML on Databricks.
👉 Read the full post here
