After establishing the foundations for monitoring machine learning systems, we now focus on a critical failure mode: drift. Models trained on historical data can lose effectiveness when the live data they encounter changes over time. Simple statistical checks on individual features often prove insufficient for detecting subtle or complex shifts in production environments.

This chapter introduces more sophisticated techniques for identifying these changes. You will learn about:

• The limitations of basic drift detection methods.
• Methods for detecting multivariate data drift, considering relationships between features.
• Sequential analysis techniques for potentially faster detection of distribution changes.
• Strategies specifically designed to identify concept drift, where the relationship between inputs and the target variable changes.
• Using adversarial validation to quantify distribution differences.
• Approaches for monitoring drift in less structured data, such as embeddings.
• How to design and implement custom drift detection logic tailored to specific needs.

We will explore the theory behind these methods and provide practical guidance, culminating in a hands-on exercise implementing a multivariate drift detection mechanism.