Ax: A Platform for Adaptive Experimentation

Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, David Eriksson, Mia Garrard, Sam Daulton, Maximilian Balandat, Eytan Bakshy, Elena Kashtelyan, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin et al. (1 additional authors not shown)

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TL;DR: Ax is a versatile, extensive, robust platform for adaptive experimentation, including AutoML

Abstract: Optimizing industry-scale machine learning systems involves resource-intensive black-box optimization. Adaptive experimentation substantially improves the sample efficiency of such tasks compared with naive baselines (such as grid or random search) by utilizing surrogate models and sequential optimization algorithms. Ax (https://ax.dev) is an open-source platform for adaptive experimentation. Ax is highly extensible and full-featured, and is used at scale at Meta. We discuss Ax's design, usage, and performance. Off the shelf, Ax achieves state-of-the-art performance in a wide range of synthetic and real-world black-box optimization tasks in machine learning, engineering, and science.

Submission Number: 9