Whether you are downloading a digital version or reading a physical copy, Designing Machine Learning Systems is highly recommended for:
: High-throughput inference computed periodically and cached in a database for fast lookups.
is a deep dive into transforming raw data into features that models can learn from. It covers handling missing values, scaling, encoding categorical variables, feature crossing, and—critically—detecting and preventing data leakage, a subtle but devastating source of overfitting. Designing Machine Learning Systems By Chip Huyen Pdf
What specific are you currently trying to scale?
Huyen's personal story is also inspiring. She grew up "chasing grasshoppers in a small rice-farming village in Vietnam" before moving to the United States, graduating from Stanford, and becoming a bestselling author. Since the book's publication, it has become an and has been translated into more than 10 languages, including Japanese, Korean, Spanish, Polish, and both simplified and traditional Chinese. She followed up with a second book, AI Engineering (2025), which became the most-read book on the O'Reilly platform since its launch. Whether you are downloading a digital version or
A model is not a "fire-and-forget" artifact; it is a living system. In this chapter, Huyen provides a playbook for the post-deployment world. She explains how to implement continuous monitoring to detect data drift (changes in input data) and concept drift (changes in the relationship between input and output), how to set up prediction serving, and how to establish feedback loops for continuous learning. This final piece of the puzzle turns a static model into a dynamic, adaptive system that improves over time.
Changes in the relationship between input and output. System Health: Monitoring latency and throughput. The Focus on Production-Ready Systems What specific are you currently trying to scale
: Research prioritizes accuracy. Production balances latency, cost, and fairness.
tackles the challenge of acquiring and preparing data for training. It discusses various sampling techniques, labeling strategies, handling class imbalance, and data augmentation methods.
Getting a model to serve predictions efficiently requires matching the business use case to the correct engineering pattern.