Most Java teams deploy ML models via a Python sidecar service, adding operational complexity. Deep Netts is a pure-Java deep learning library that eliminates this by letting you train, serialize, and serve models entirely within a Spring Boot application. This tutorial walks through building a production-ready fraud scoring service: training a feed-forward neural network on the Kaggle credit card fraud dataset, exporting both the model and its scaler as versioned artifacts, wrapping them in a thread-safe Spring Boot REST service using an object pool, configuring a deliberate decision threshold, adding health checks and regression tests, and handling deployment concerns like Dockerization, Kubernetes readiness probes, and licensing restrictions. Key pitfalls covered include the model/scaler mismatch bug, network statefulness requiring pooling, and the need for drift monitoring and shadow-mode rollout before going live.
