Design effective & reliable machine learning systems!

Machine learning system design is complex. The successful ML engineer needs to navigate a multistep process that demands skills from many different fields and roles. 

Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale. 

Authors Arseny Kravchenko and Valeri Babushkin have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.

"This is not about MLOps! This book is about a more important question: how to make sure that an ML project does not end up on the shelf?"

—Boris Tseytlin, Senior Machine Learning Engineer, Planet Farms

In the book you’ll follow two example companies each building a new ML system, exploring how their needs are expressed in design documents and learning best practices by writing your own. Along the way, you’ll learn how to ace ML system design interviews, even at highly competitive FAANG-like companies, and improve existing ML systems by identifying bottlenecks and optimizing system performance.

Machine Learning System Design is available from its publisher Manning Publications.

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One free eBook code for Machine Learning System Design: mlstds-AEBF