This book explores how senior machine learning engineers think, make decisions, and design real-world ML systems beyond models and algorithms. It focuses on mental models, tradeoffs, and failure modes that only appear in production, such as data drift, feedback loops, system reliability, monitoring, and ethical responsibility. Rather than teaching how to train models, the book explains how to frame problems correctly, evaluate impact, manage risk, and decide when machine learning should-or should not-be used. It is a practical guide to judgment, system thinking, and responsibility in modern machine learning engineering.
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Abdelfattah Ragab is an experienced software developer with over 20 years of experience in the field. In his long career, he has specialized in various aspects of web development, especially front-end technologies. He is known for his expertise in frameworks such as Angular and React, as well as his knowledge of HTML5 and CSS. Abdelfattah is also a successful author who has written several books for beginners and experienced developers. His works include titles such as "Responsive Layouts: Flex, Grid & Multi-Column" and "Angular Shopping Store"," which provide a comprehensive insight into modern web development practices. These books cover not only basic concepts but also advanced techniques, making them valuable resources for anyone looking to improve their skills in web technologies. With his publications, Abdelfattah aims to bridge the gap between theoretical knowledge and practical application. His books often include practical projects, such as developing e-commerce applications and integrating payment systems like Stripe, which help readers gain real-world experience. This approach reflects his commitment to providing his readers with a deeper understanding of web development. In addition to his writing, Abdelfattah works as a front-end lead, which allows him to stay up to date on industry trends and best practices.
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Paperback. Zustand: new. Paperback. This book explores how senior machine learning engineers think, make decisions, and design real-world ML systems beyond models and algorithms. It focuses on mental models, tradeoffs, and failure modes that only appear in production, such as data drift, feedback loops, system reliability, monitoring, and ethical responsibility. Rather than teaching how to train models, the book explains how to frame problems correctly, evaluate impact, manage risk, and decide when machine learning should-or should not-be used. It is a practical guide to judgment, system thinking, and responsibility in modern machine learning engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783384802163
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores how senior machine learning engineers think, make decisions, and design real-world ML systems beyond models and algorithms. It focuses on mental models, tradeoffs, and failure modes that only appear in production, such as data drift, feedback loops, system reliability, monitoring, and ethical responsibility. Rather than teaching how to train models, the book explains how to frame problems correctly, evaluate impact, manage risk, and decide when machine learning should-or should not-be used. It is a practical guide to judgment, system thinking, and responsibility in modern machine learning engineering. 98 pp. Englisch. Bestandsnummer des Verkäufers 9783384802163
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Paperback. Zustand: new. Paperback. This book explores how senior machine learning engineers think, make decisions, and design real-world ML systems beyond models and algorithms. It focuses on mental models, tradeoffs, and failure modes that only appear in production, such as data drift, feedback loops, system reliability, monitoring, and ethical responsibility. Rather than teaching how to train models, the book explains how to frame problems correctly, evaluate impact, manage risk, and decide when machine learning should-or should not-be used. It is a practical guide to judgment, system thinking, and responsibility in modern machine learning engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9783384802163
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores how senior machine learning engineers think, make decisions, and design real-world ML systems beyond models and algorithms. It focuses on mental models, tradeoffs, and failure modes that only appear in production, such as data drift, feedback loops, system reliability, monitoring, and ethical responsibility. Rather than teaching how to train models, the book explains how to frame problems correctly, evaluate impact, manage risk, and decide when machine learning should-or should not-be used. It is a practical guide to judgment, system thinking, and responsibility in modern machine learning engineering.tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg 100 pp. Englisch. Bestandsnummer des Verkäufers 9783384802163
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