At Monetate, we’ve deployed Bayesian bandits (both noncontextual and contextual) to help our clients optimize their e-commerce sites since early 2016. This talk is an overview of the lessons we’ve learned from both the processes of deploying real-time Bayesian machine learning systems at scale and building a data product on top of these systems that is accessible to non-technical users (marketers). This talk will cover:

• The place of multi-armed bandits in the A/B testing industry,
• Thompson sampling and the basic theory of Bayesian bandits,
• Bayesian approaches for accommodating nonstationarity in bandit feedback,
• User experience challenges in driving adoption of these technologies by nontechnical marketers.

We will focus primarily on noncontextual bandits and give a brief overview of these problems in the contextual setting as time permits. Bayesian bandit concepts will be illustrated through simulations in Python.