  The bandit paradigm provides a unified modeling framework for problems that
require decision-making under uncertainty. Because many business metrics can be
viewed as rewards (a.k.a. utilities) that result from actions, bandit
algorithms have seen a large and growing interest from industrial applications,
such as search, recommendation and advertising. Indeed, with the bandit lens
comes the promise of direct optimisation for the metrics we care about.
  Nevertheless, the road to successfully applying bandits in production is not
an easy one. Even when the action space and rewards are well-defined,
practitioners still need to make decisions regarding multi-arm or contextual
approaches, on- or off-policy setups, delayed or immediate feedback, myopic or
long-term optimisation, etc. To make matters worse, industrial platforms
typically give rise to large action spaces in which existing approaches tend to
break down. The research literature on these topics is broad and vast, but this
can overwhelm practitioners, whose primary aim is to solve practical problems,
and therefore need to decide on a specific instantiation or approach for each
project. This tutorial will take a step towards filling that gap between the
theory and practice of bandits. Our goal is to present a unified overview of
the field and its existing terminology, concepts and algorithms -- with a focus
on problems relevant to industry. We hope our industrial perspective will help
future practitioners who wish to leverage the bandit paradigm for their
application.
