Winning with Multi-Armed Bandits: Smarter Experimentation in Databricks
Posted on August 18th, 2025
Running experiments often feels like gambling. Should you put more volume behind variant A, or give variant B another chance? Traditional A/B testing splits traffic and waits - but what if you could continuously adapt, maximizing gains as you learn? Enter Multi-Armed Bandits: an elegant blend of probability, statistics, and decision-making that turns experiments into dynamic optimization engines.
Just like choosing the right slot machine at a casino, Multi-Armed Bandits help you decide which option deserves your next coin - except here, the coin is traffic, impressions, or user attention. Let’s explore how they work, why they beat static testing, and how we’ve applied them in Databricks.
