Efficient tuning of online systems using Bayesian optimization

This post introduces the application of Bayesian optimization, an AI technique, to efficiently tune the numerous parameters within Meta's large backend systems, particularly machine learning models. It details how this method adaptively designs A/B tests based on prior results, enabling joint tuning of more parameters with fewer experiments compared to traditional methods like grid search or manual tuning, and has been successfully applied to dozens of parameter tuning experiments.