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Machine Learning System Design: Real-World Lessons : Deep Dives into Stripe, Spotify, Netflix, and Meta's ML Architectures
Designing robust, scalable, and ethical machine learning systems for real-world applications is vastly different from building models in a lab. "Machine Learning System Real-World Lessons" bridges this critical gap, providing a holistic, engineering-first approach to operationalizing AI.
This book takes you beyond algorithms and into the full ML lifecycle. Discover the fundamental choices between online and batch inference, and master how Docker and Kubernetes power efficient, scalable deployments. Dive deep into the nuances of MLOps, understanding continuous monitoring for data and model drift, and implementing advanced strategies for automated retraining and safe, controlled model rollouts using A/B testing and canary deployments.
What truly sets this book apart are its unprecedented deep dives into the ML architectures of tech giants:
Unravel the meticulously engineered fraud detection systems protecting billions in transactions.
Explore the sophisticated recommendation engines that curate personalized listening experiences for millions.
Learn how their personalization and content delivery systems engage and retain a global subscriber base.
Gain insight into the vast infrastructure supporting social applications, from content ranking to robust moderation.
Beyond the technical blueprints, we confront the vital human considerations. Understand how to identify and mitigate algorithmic bias, and tackle the "black box" problem with practical Explainable AI (XAI) techniques. Learn to build trustworthy and accountable AI by integrating ethical guidelines, privacy-by-design, and clear governance.
"Machine Learning System Design" is an indispensable guide for data scientists looking to productionize their work, ML engineers aiming to build resilient systems, and anyone eager to understand the engineering rigor behind today's most impactful AI applications. This book will transform your understanding of what it truly takes to bring intelligent systems to life.
This book takes you beyond algorithms and into the full ML lifecycle. Discover the fundamental choices between online and batch inference, and master how Docker and Kubernetes power efficient, scalable deployments. Dive deep into the nuances of MLOps, understanding continuous monitoring for data and model drift, and implementing advanced strategies for automated retraining and safe, controlled model rollouts using A/B testing and canary deployments.
What truly sets this book apart are its unprecedented deep dives into the ML architectures of tech giants:
Unravel the meticulously engineered fraud detection systems protecting billions in transactions.
Explore the sophisticated recommendation engines that curate personalized listening experiences for millions.
Learn how their personalization and content delivery systems engage and retain a global subscriber base.
Gain insight into the vast infrastructure supporting social applications, from content ranking to robust moderation.
Beyond the technical blueprints, we confront the vital human considerations. Understand how to identify and mitigate algorithmic bias, and tackle the "black box" problem with practical Explainable AI (XAI) techniques. Learn to build trustworthy and accountable AI by integrating ethical guidelines, privacy-by-design, and clear governance.
"Machine Learning System Design" is an indispensable guide for data scientists looking to productionize their work, ML engineers aiming to build resilient systems, and anyone eager to understand the engineering rigor behind today's most impactful AI applications. This book will transform your understanding of what it truly takes to bring intelligent systems to life.
175 pages, Kindle Edition
Published July 29, 2025
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