Adaptive Experimentation with Meta’s Ax: A Practical Coding Guide
In this tutorial, we explore adaptive experimentation using Meta’s Ax with the modern Client API. We work through a complete workflow where we tune a RandomForest model on a synthetic classification dataset while balancing predictive accuracy against model footprint. We begin by defining a mixed search space with integer, float, log-scaled, and categorical parameters, then use Ax’s ask-tell optimization loop to run constrained Bayesian optimization, multi-objective optimization, and parameter-c
This is a practical guide to adaptive experimentation, a method for systematically tuning machine learning model settings through an optimization loop. The tutorial demonstrates how to balance competing goals, such as maximizing predictive accuracy while keeping model size small, by defining a search space of parameters and letting an optimization system suggest configurations to test. The approach includes constrained single-objective optimization, where accuracy is maximized within a model size limit, and multi-objective optimization, where multiple goals are considered together to identify trade-off solutions. Understanding this matters because it automates the process of finding good hyperparameter settings rather than testing them manually, and handles situations where different performance metrics must be weighed against each other.
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