Event-Driven ML Architecture - Interactive Visualization

Event-driven ML pipelines decouple data producers from ML consumers through a persistent event log (Kafka). Events flow from sources - user actions, IoT sensors, financial transactions - into Kafka topics, then through a stream processor (Flink or Spark Streaming) that computes features, calls the model, and emits predictions. Consumer lag is the critical health metric: when lag grows, your ML pipeline is falling behind the event stream, meaning predictions are delivered late. Multiple consumer groups allow multiple models to independently consume the same event stream.

• Consumer lag: the number of events in the Kafka queue that have not yet been processed - zero lag means real-time
• At-least-once delivery: messages may be delivered more than once; consumers must be idempotent
• Exactly-once semantics: transactional Kafka + idempotent producers guarantee no duplicates and no loss - 2x overhead
• Backpressure: when processing rate falls below event rate, lag grows - circuit breakers or auto-scaling are required

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