Deploying a machine learning model into a live environment introduces a distinct set of operational requirements compared to traditional software systems. Models interact with changing data distributions and their performance can degrade silently over time due to factors like data drift or concept drift. Effective monitoring is essential not just for maintaining performance but also for ensuring reliability and managing operational risks.

This chapter lays the foundation for understanding and implementing production ML monitoring systems. We will begin by identifying the unique challenges associated with tracking the health of ML models after deployment. We'll then define the comprehensive scope required for monitoring, encompassing input data, model predictions, performance metrics, and the supporting infrastructure. You will learn how to establish meaningful Service Level Objectives (SLOs) specifically for ML applications. Finally, we will review common architectural patterns used to build these monitoring systems and discuss how monitoring integrates into the broader MLOps lifecycle. By the end of this chapter, you will have a clear understanding of the core concepts and considerations for building effective ML monitoring strategies.