Testing Machine Learning Systems

This chapter introduces the process of evaluating machine learning systems, bridging the gap between offline metrics and real-world online performance. We will explore the evaluation funnel, a structured framework that guides a model from initial offline validation to a live A/B test, ensuring that only the most promising candidates are exposed to users. We will cover the distinct challenges of testing different components of an ML system, from features to model architectures, and discuss strategies for managing the “cold-start” problem when deploying models that rely on user interaction data.