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Many online applications running on live traffic are powered by machinelearning models, for which training, validation, and hyper-parameter tuning areconducted on historical data. However, it is common for models demonstratingstrong performance in offline analysis to yield poorer performance whendeployed online. This problem is a consequence of the difficulty of training onhistorical data in non-stationary environments. Moreover, the machine learningmetrics used for model selection may not sufficiently correlate with real-worldbusiness metrics used to determine the success of the applications beingtested. These problems are particularly prominent in the Real-Time Bidding(RTB) domain, in which ML models power bidding strategies, and a change inmodels will likely affect performance of the advertising campaigns. In thiswork, we present Automatic Model Selector (AMS), a system for scalable onlineselection of RTB bidding strategies based on real-world performance metrics.AMS employs Multi-Armed Bandits (MAB) to near-simultaneously run and evaluatemultiple models against live traffic, allocating the most traffic to thebest-performing models while decreasing traffic to those with poorer onlineperformance, thereby minimizing the impact of inferior models on overallcampaign performance. The reliance on offline data is avoided, instead makingmodel selections on a case-by-case basis according to actionable businessgoals. AMS allows new models to be safely introduced into live campaigns assoon as they are developed, minimizing the risk to overall performance. Inlive-traffic tests on multiple ad campaigns, the AMS system proved highlyeffective at improving ad campaign performance.

Online and Scalable Model Selection with Multi-Armed Bandits