Chapter 11: Key Principles for Monitoring Your ML System
In this chapter, we will learn about the fundamental principles that are essential for monitoring your machine learning (ML) models in production. You will learn how to build trustworthy and Explainable AI solutions using the Explainable Monitoring Framework. The Explainable Monitoring Framework can be used to build functional monitoring pipelines so that you can monitor ML models in production, analyze application and model performance, and govern ML systems. The goal of monitoring ML systems is to enable trust, transparency, and explainability in order to increase business impact. We will learn about this by looking at some real-world examples.
Understanding the principles mentioned in this chapter will equip you with the knowledge to build end-to-end monitoring systems for your use case or company. This will help you engage business, tech, and public (customers and legal) stakeholders so that you can efficiently achieve...
