A bank I worked with was detecting fraud in daily batches.

They'd process the previous day's transactions every morning.

The problem: by the time they flagged a fraudulent transaction, the money was already gone. They were always 24 hours behind the criminals.

We rebuilt it as a streaming ML pipeline. Kafka for event streaming, Flink for real-time processing, Redis for feature caching, and the model served via FastAPI with WebSocket connections.

Time to detection went from 24 hours to under 50 milliseconds. The amount of prevented fraud in the first quarter alone justified the entire project cost.

This is the difference between batch ML and real-time ML. For some applications, yesterday's predictions are useless.

Streaming ML applies everywhere: dynamic pricing, live recommendation updates, IoT anomaly detection, social media trend analysis. If the real world is moving in real-time, your model should too.

The stack is more complex than batch processing, no question. But the value proposition is often so clear that it sells itself.

If your model only sees yesterday's data, you're already late.