  Many online applications running on live traffic are powered by machine
learning models, for which training, validation, and hyper-parameter tuning are
conducted on historical data. However, it is common for models demonstrating
strong performance in offline analysis to yield poorer performance when
deployed online. This problem is a consequence of the difficulty of training on
historical data in non-stationary environments. Moreover, the machine learning
metrics used for model selection may not sufficiently correlate with real-world
business metrics used to determine the success of the applications being
tested. These problems are particularly prominent in the Real-Time Bidding
(RTB) domain, in which ML models power bidding strategies, and a change in
models will likely affect performance of the advertising campaigns. In this
work, we present Automatic Model Selector (AMS), a system for scalable online
selection of RTB bidding strategies based on real-world performance metrics.
AMS employs Multi-Armed Bandits (MAB) to near-simultaneously run and evaluate
multiple models against live traffic, allocating the most traffic to the
best-performing models while decreasing traffic to those with poorer online
performance, thereby minimizing the impact of inferior models on overall
campaign performance. The reliance on offline data is avoided, instead making
model selections on a case-by-case basis according to actionable business
goals. AMS allows new models to be safely introduced into live campaigns as
soon as they are developed, minimizing the risk to overall performance. In
live-traffic tests on multiple ad campaigns, the AMS system proved highly
effective at improving ad campaign performance.
