Model Selection for Production System via Automated Online Experiments. (arXiv:2105.13420v1 [stat.ML])

A challenge that machine learning practitioners in the industry face is the
task of selecting the best model to deploy in production. As a model is often
an intermediate component of a production system, online controlled experiments
such as A/B tests yield the most reliable estimation of the effectiveness of
the whole system, but can only compare two or a few models due to budget
constraints. We propose an automated online experimentation mechanism that can
efficiently perform model selection from a large pool of models with a small
number of online experiments. We derive the probability distribution of the
metric of interest that contains the model uncertainty from our Bayesian
surrogate model trained using historical logs. Our method efficiently
identifies the best model by sequentially selecting and deploying a list of
models from the candidate set that balance exploration-exploitation. Using
simulations based on real data, we demonstrate the effectiveness of our method
on two different tasks.



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