Most of us got into data science because it’s exciting (if chaotic) and there’s a constant stream of new ideas, which is thrilling (if intimidating). But if learning is all about keeping up, why can’t our models do it?

Real-time ML models continually learn on new data as soon as it arrives, so they’re less susceptible to concept drift and data drift. Read on to learn how to use River and Vowpal Wabbit to build real-time models in Python.

A few weeks ago, I’d never used an event stream before. Read on to hear how I built my first event-driven data science app — the biggest challenges, my lessons learned, and a couple of key takeaways!