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. ...
Get Platform and Model Design for Responsible AI now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
