Ax
An accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments using Bayesian and bandit optimization.
What is Ax?
Ax is an adaptive experimentation platform that uses machine learning to guide the iterative exploration of parameter spaces in order to identify optimal configurations efficiently. It supports Bayesian optimization and bandit optimization as exploration strategies, abstracting complex details to make advanced techniques accessible. The platform is designed for real-world deployment, handling complex search spaces, multiple objectives, constraints, and noisy observations.
Data scientists, machine learning engineers, and researchers who need to optimize parameters, hyperparameters, or configurations in resource-constrained environments. It is also suitable for practitioners seeking to apply state-of-the-art Bayesian optimization without deep expertise in optimization algorithms.
Developers choose Ax for its expressive API that handles real-world complexity, strong out-of-the-box performance powered by BoTorch, and production-ready features for scalable deployment. It uniquely balances accessibility for practitioners with flexibility for researchers to innovate.
Overview
Adaptive Experimentation Platform
Use Cases
Best For
- Hyperparameter tuning for machine learning models
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