Platform and Model Design for Responsible AI

11

The Ethics of Model Adaptability

This chapter gives a detailed overview of detecting different types of model drift for model governance purposes in organizations. The primary objective of this chapter is to demonstrate variations of ML models, with multiple examples to give you an awareness of the importance of the different statistical measures available for detecting data changes and model metric variations. This will help data scientists and MLOps professionals to choose the right drift detection mechanisms and stick to the correct model metric performance thresholds to control risks arising due to incorrect predictions. You’ll learn how to quantify and explain model drift and answer questions related to the need for model calibration. ...