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Engineering Machine Learning: Designing Infrastructure, Pipelines, and Deployment Workflows for Modern Machine Learning Systems
Engineering Machine Designing Infrastructure, Pipelines, and Deployment Workflows for Modern Machine Learning Systems
Machine learning models don't succeed in isolation—they thrive in systems. This book is your practical blueprint for building and scaling robust, production-grade ML systems from end to end. Designed for engineers, architects, MLOps professionals, and technical leaders, it offers deep, hands-on guidance on how to bridge the gap between experimental models and real-world applications that serve users reliably and efficiently.
You’ll learn how to construct modular data pipelines, manage model versioning and reproducibility, orchestrate CI/CD workflows tailored for ML, and deploy models across cloud, edge, and hybrid environments. Each chapter dissects a critical component of the machine learning lifecycle—from data ingestion, feature storage, and distributed training to monitoring model drift and enabling retraining pipelines. The book also addresses the human side of machine learning governance, security, fairness, and compliance with regulations like GDPR and HIPAA.
With rich examples, real-world case studies, and production-tested patterns, this book goes beyond frameworks and APIs. It gives you the architectural thinking, infrastructure patterns, and operational playbooks to deliver scalable, maintainable, and trustworthy ML systems at scale.
If you're ready to stop shipping notebooks and start engineering real ML systems—read this book, implement what works, and lead the next generation of intelligent software.
Machine learning models don't succeed in isolation—they thrive in systems. This book is your practical blueprint for building and scaling robust, production-grade ML systems from end to end. Designed for engineers, architects, MLOps professionals, and technical leaders, it offers deep, hands-on guidance on how to bridge the gap between experimental models and real-world applications that serve users reliably and efficiently.
You’ll learn how to construct modular data pipelines, manage model versioning and reproducibility, orchestrate CI/CD workflows tailored for ML, and deploy models across cloud, edge, and hybrid environments. Each chapter dissects a critical component of the machine learning lifecycle—from data ingestion, feature storage, and distributed training to monitoring model drift and enabling retraining pipelines. The book also addresses the human side of machine learning governance, security, fairness, and compliance with regulations like GDPR and HIPAA.
With rich examples, real-world case studies, and production-tested patterns, this book goes beyond frameworks and APIs. It gives you the architectural thinking, infrastructure patterns, and operational playbooks to deliver scalable, maintainable, and trustworthy ML systems at scale.
If you're ready to stop shipping notebooks and start engineering real ML systems—read this book, implement what works, and lead the next generation of intelligent software.
274 pages, Kindle Edition
Published June 11, 2025
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