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Published : Apr 26, 2023
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar. Understand more
Apr 2023
Trial ?

Faced with the challenge of exploring large configuration spaces, where it may take a significant amount of time to evaluate a given configuration, teams can turn to adaptive experimentation, a machine-guided, iterative process to find optimal solutions in a resource-efficient manner. Ax is a platform for managing and automating adaptive experiments, including machine learning experiments, A/B tests and simulations. Currently, it supports two optimization strategies: Bayesian optimization using BoTorch, which is built on top of PyTorch, and contextual bandits. Facebook, when releasing Ax and BoTorch, described use cases like increasing the efficiency of back-end infrastructure, tuning ranking models and optimizing hyperparameter search for a machine learning platform. We've had good experiences using Ax for a variety of use cases, and while tools for hyperparameter tuning exist, we're unaware of a platform that provides functionality in a scope similar to Ax.

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