Designing Machine Learning Systems
by Chip Huyen
Chip Huyen's practical guide to building machine learning systems that work in production.
"ML algorithms don't predict the future, but encode the past, thus perpetuating biases in the data.".
Editorial Summary
Chip Huyen's 'Designing Machine Learning Systems' shifts focus from model development to the entire lifecycle of deploying machine learning in real-world applications. Rather than emphasizing deep learning architectures or mathematical theory, Huyen addresses the engineering challenges practitioners face: data pipelines, feature engineering, model monitoring, and handling concept drift in production environments. The book draws on Huyen's experience at companies like Netflix and Snorkel AI to provide concrete patterns for building reliable systems that maintain performance over time. What distinguishes this work is its emphasis on the full system design rather than isolated model optimization, making it essential for engineers building machine learning infrastructure at scale.
Perspective
"Machine learning engineers and data scientists shipping models to production need this book now, as companies deploying ChatGPT integrations and large language models increasingly discover that model selection is only 10% of the battle. The real challenges—data quality, retraining pipelines, and monitoring drift—are what separate experimental notebooks from systems that actually serve users reliably."
Matched by concept and theme
