Question

You have trained a loan-approval classification model registered in your Azure Machine Learning workspace. Before promoting it to production, your compliance team requires an assessment of whether the model performs equitably across applicant age groups and produces explanations for individual predictions. You want to generate a single consolidated view that includes error analysis, feature importance, and fairness metrics without writing custom evaluation code for each component. What should you do?

Accepted Answer

Create a Responsible AI dashboard by assembling the Responsible AI components (error analysis, explanation, and fairness assessment) into an Azure Machine Learning pipeline job.

Answer

Enable data drift monitoring on the model's deployment endpoint and review the drift signal over a 30-day baseline window.

Answer

Configure an AutoML classification job and inspect the model explanation tab that is automatically produced for the best child run.

Answer

Deploy the model to a managed online endpoint and enable Application Insights to capture per-request inference logs for later analysis.