Question

You are training a classification model in Azure Machine Learning using a command job that logs a validation metric called 'val_auc' with MLflow. You want to automate hyperparameter tuning to maximize this metric across combinations of learning rate and batch size, sampling values randomly and stopping poorly performing trials early. Which approach should you use to configure the tuning job?

Accepted Answer

Convert the command job into a sweep job, define the search space with random sampling, set the primary_metric to 'val_auc' with goal 'maximize', and attach a bandit early termination policy.

Answer

Submit multiple independent command jobs with different fixed hyperparameter values and manually compare their logged 'val_auc' values in the studio.

Answer

Create a sweep job with grid sampling, set the primary_metric to 'val_auc' with goal 'minimize', and omit an early termination policy so all trials complete.

Answer

Register the command job as a component in a pipeline and configure parallel steps that each use a different learning rate hard-coded in the script.