Chapter 13: Governing the ML System for Continual Learning

In this chapter, we will reflect on the need for continual learning in machine learning (ML) solutions. Adaptation is at the core of machine intelligence. The better the adaptation, the better the system. Continual learning focuses on the external environment and adapts to it. Enabling continual learning for an ML system can reap great benefits. We will look at what is needed to successfully govern an ML system as we explore continuous learning and study the governance component of the Explainable Monitoring Framework, which helps us control and govern ML systems to achieve maximum value.

We will delve into the hands-on implementation of governance by enabling alert and action features. Next, we will look into ways of assuring quality for models and controlling deployments, and we'll learn the best practices to generate model audits and reports. Lastly, we will learn about methods to enable model retraining and maintain...