Running Selective Parts of an Azure ML Experiment

This post is authored by Mickaël Savafi, Software Engineer at Microsoft. Azure ML is a drag and drop tool that lets users create machine learning experiments. Experiments are typically constructed by adding modules, tweaking parameters and validating these changes by running the experiment. For every change, however, validation requires running the entire experiment again, and so that can sometimes get quite tedious and time consuming. Today, we are excited to introduce the ability for users to select a portion of their experiment graph for execution.