How can researchers and engineers explore large configuration spaces that have complex trade-offs when it may take hours or days to evaluate any given configuration? This challenge frequently arises across many domains, including tuning hyperparameters for machine learning (ML) models, finding optimal product settings through A/B testing, and designing next-generation hardware.

Today we are open-sourcing two tools, Ax and BoTorch, that enable anyone to solve challenging exploration problems in both research and production — without the need for large quantities of data.

• Ax is an accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.

• BoTorch, built on PyTorch, is a flexible, modern library for Bayesian optimization, a probabilistic method for data-efficient global optimization.

See the full blog post on the AI website.