Discover how to build a fault-tolerant streaming architecture using Apache Flink and Kotlin. This guide demonstrates applying Online Machine Learning to autonomously detect concept drift and correct for physical machinery wear in real-time, which is safely managed by a deterministic Shadow Mode router.

In Part 1, we built a contextual bandit prototype using Python and Mab2Rec. While effective for testing algorithms locally, a monolithic script cannot handle production scale. Real-world recommendation systems require low-latency inference for users and high-throughput training for model updates.

This post demonstrates how to decouple these concerns using an event-driven architecture with Apache Flink, Kafka, and Redis.

Traditional recommendation systems often struggle with cold-start users and with incorporating immediate contextual signals. In contrast, Contextual Multi-Armed Bandits, or CMAB, learn continuously in an online setting by balancing exploration and exploitation using real-time context. In Part 1, we develop a Python prototype that simulates user behavior and validates the algorithm, establishing a foundation for scalable, real-time recommendation systems.