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.

Summary and Contributions: The paper proposes a model selection algorithm called Model Selection with Automated Online Experiments (AOE) that is designed for use in production systems. In the problem statement, it is stated that the goal of the model selection problem is to select the model from a set of candidate models that maximises a metric of interest. It is assumed that the metric of interest can be expressed as the average immediate feedback from each of a model's predictions. AOE uses both historical log data and data collected from a small budget of online experiments to inform the choice of model. A distribution for the accumulative metric, or expected immediate feedback, is derived.

This paper proposes an extension to Bayesian optimization methods for model selection. A surrogate model for the dataset is added to the setup, which allows the optimization to take more information to account as data is collected over time. The reviewers generally thought this was an interesting approach and an important direction. The main debate focused on the significance of the synthetic results based on real data, and whether they can be expected to generalize. We think that the clear novelty and the positive results outweigh this weakness.

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.