ml-inference

What problem does it solve?

This Skill explains how to operate the online anomaly scoring service that classifies streaming transaction events, maintains model state, and keeps inference behavior stable in production.

Core Features & Use Cases

• Live anomaly scoring: Combines River HalfSpaceTrees with a custom amount-based deviation scorer to detect suspicious events as they arrive.
• Operational model control: Handles dynamic threshold adjustment, Redis-backed persistence, and hot-swapping of promoted models without restarting the service.
• Use Cases: Use it when you need to debug Kafka-driven fraud scoring, verify score-before-learn behavior, or keep online learning and deployment logic synchronized across services.

Quick Start

Use this Skill to review the ml-inference service flow, scoring logic, and operational safeguards from the repository context.