Designing Machine Learning Systems | Chapter 8: Data Distribution Shifts & Monitoring
About this lesson
Read the detailed version on: https://onepagecode.substack.com/ Use this url: https://onepagecode.substack.com/p/large-language-models-architectures In Chapter 8 of "Designing Machine Learning Systems" by Chip Huyen, we tackle one of the most critical challenges in production ML: why models that perform well during development often fail after deployment. This chapter begins by examining the root causes of ML system failures, distinguishing between traditional software failures and ML-specific failures. We then take a deep dive into data distribution shifts — the phenomenon where production data diverges from training data — covering covariate shift, label shift, and concept drift with clear examples. We also explore two other major ML-specific failure modes: edge cases (where models fail catastrophically on rare inputs) and degenerate feedback loops (where a model’s predictions influence its own future training data, amplifying biases over time). The chapter then focuses on practical monitoring and observability strategies. We discuss what to monitor (accuracy metrics, predictions, features, and raw inputs), how to detect distribution shifts using statistical methods and time-scale windows, and the tools required for effective monitoring — including logs, dashboards, and actionable alerts. Finally, we cover how to make ML systems more observable so that when something goes wrong, teams can quickly diagnose whether the issue stems from data shifts, pipeline bugs, or model degradation. What you’ll learn in this chapter: • Common causes of ML system failures in production • Covariate shift, label shift, and concept drift explained • Edge cases and degenerate feedback loops • How to detect data distribution shifts statistically • Monitoring strategies for accuracy, predictions, and features • Logs, dashboards, alerts, and observability best practices • Building robust ML systems that adapt to changing data This chapter is essential for anyone responsible for k
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