Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.

In Machine Learning System Design: With end-to-end examples you will learn:

The big picture of machine learning system design
Analyzing a problem space to identify the optimal ML solution
Ace ML system design interviews
Selecting appropriate metrics and evaluation criteria
Prioritizing tasks at different stages of ML system design
Solving dataset-related problems through data gathering, error analysis, and feature engineering
Recognizing common pitfalls in ML system development
Designing ML systems to be lean, maintainable, and extensible over time

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.

About the Author

Arseny Kravchenko is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups, including real-time video processing, manufacturing optimization, and financial transactions analysis.

Valerii Babushkin is an accomplished data science leader with extensive experience in the tech industry. He currently serves as the VP of Data Science at Blockchain.com, where he is responsible for leading the company's data-driven initiatives. Prior to joining Blockchain.com, Valerii held key roles at leading tech companies, such as Facebook, Alibaba, and X5 Retail Group.

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