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Published : Apr 26, 2023
Apr 2023
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

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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