Ax 1.0 is an open-source platform designed to enhance complex experimentation in AI and machine learning. It leverages machine learning techniques, specifically Bayesian optimization, to streamline the process of exploring and optimizing numerous configurations. Researchers at Meta use Ax extensively for tasks like hyperparameter tuning, infrastructure optimization, and even hardware design for AR/VR applications. The platform’s efficiency is crucial when evaluating configurations can be both time-consuming and resource-heavy. The accompanying paper, “Ax: A Platform for Adaptive Experimentation,” details Ax's architecture and methodology, comparing it to other optimization libraries. Ax excels in adaptive experimentation by proposing new configurations based on insights from previous evaluations. Users can not only improve their systems but also gain a better understanding of the underlying mechanics through various analytical tools provided by Ax, such as sensitivity analysis and performance visualizations. The platform is built on a flexible Gaussian process that models the optimization problem, allowing it to make predictions while quantifying uncertainty. This is particularly beneficial when dealing with numerous parameters, making Ax suited for high-dimensional optimization challenges. At Meta, Ax has tackled complex goals, such as balancing machine learning model accuracy with resource efficiency, and even optimizing concrete mixes for sustainability initiatives. Its data-driven approach helps researchers navigate the intricacies of system optimization effectively.