Why Ax?

Developers and researchers alike face problems which confront them with a large space of possible ways to configure something –– whether those are learning rates or other hyperparameters in machine learning, "magic numbers" used for infrastructure or compiler flags, or design parameters in a physical engineering task. Selecting and tuning these configurations can often take time, resources, and affects the quality of user experiences. Ax is a machine learning system to help guide and automate this experimentation process, so that researchers and developers can determine how to get the most out of their processes in an efficient manor.

Ax is a platform for optimizing many kinds of experiment, and is typically useful for problems that are expensive to evaulate or where the number of evaluations must remain limited. Machine learning experiments, A/B tests, and costly simulations are contexts in which adaptive experimentation techniques are especially useful. Ax can optimize continuous (e.g., integer or floating point)-valued configurations, discrete configurations (e.g., variants of an A/B test), or mixed spaces using techniques like Bayesian optimization. This makes it suitable for a wide range of applications.

Unique capabilities

• Expressive API: Ax has an expressive API that can address many real-world optimization tasks. It handles complex search spaces, multiple objectives, constraints on both parameters and outcomes, and noisy observations. It supports suggesting multiple designs to evaluate in parallel (both synchronously and asynchronously) and the ability to early-stop evaluations.

• Strong performance out of the box: Ax abstracts away optimization details that are important but obscure, providing sensible defaults and enabling practitioners to leverage advanced techniques otherwise only accessible to optimization experts.

• State-of-the-art methods: Ax leverages state-of-the-art Bayesian optimization algorithms implemented in BoTorch, to deliver strong performance across a variety of problem classes.

• Flexible: Ax is highly configurable, allowing researchers to plug in novel optimization algorithms, models, and experimentation flows.

• Production ready: Ax offers automation and orchestration features as well as robust error handling for real-world deployment at scale.