A tutorial has been published on using Meta's Ax for adaptive experimentation in machine learning. The tutorial demonstrates how to use Ax with the modern Client API to balance predictive accuracy against model size. It involves defining a search space with various parameter types and tuning a RandomForest model on a synthetic classification dataset. The goal is to optimize the model's performance while minimizing its computational footprint. This approach can help machine learning practitioners make more informed decisions about their models. This matters because it can lead to more efficient and effective model development.