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Designing a machine learning system is complex. A successful ML engineer must navigate a multi-step process that requires skills in various disciplines and roles.
Machine Learning System Design: A practical guide to planning and designing successful ML applications with end-to-end examples. It provides a clear, repeatable framework for building, maintaining, and improving systems of all sizes.
Authors Arseny Kravchenko and Valeri Babushkin have packed campfire stories and personal tips from their own extensive careers into this unique handbook. You can learn directly from their experience as you explore every aspect of machine learning systems, from requirements gathering to data sourcing to deploying and managing the complete system.
“This is not a book about MLOps. This book addresses the more important question: How do I keep my ML projects from getting shelved?”
—Boris Tseytlin, Senior Machine Learning Engineer, Planet Farms
This book follows the example of two companies, each building a new ML system, explores how their needs are expressed in design documents, and learns best practices by creating their own systems. . Along the way, even a highly competitive company like FAANG will pass ML system design interviews and learn how to improve existing ML systems by identifying bottlenecks and optimizing system performance. .
Machine Learning System Design is available from the publisher Manning Publications.
35% discount code (applies to all products in all formats): nlkdnuggets21
1 free eBook code for Machine Learning System Design: mlstds-AEBF
