Model Selection for Production System via Automated Online Experiments

TL;DR

This paper introduces an automated online experiment method for efficiently selecting the best model from many candidates in production systems, using Bayesian surrogate models and sequential exploration.

Contribution

It presents a novel automated online experimentation framework that leverages Bayesian surrogate models to optimize model selection with limited experiments.

Findings

Effective identification of the best model in simulations

Balances exploration and exploitation efficiently

Demonstrates success on real-data-based tasks

Abstract

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.