What are the main topics covered in 'Machine Learning Engineering'?

The book covers a range of fundamental topics including supervised and unsupervised learning, model deployment, and performance evaluation. It emphasizes engineering principles essential for building robust machine learning systems. By addressing real-world applications and challenges, it prepares readers to implement and manage machine learning projects efficiently.

Who is the target audience for 'Machine Learning Engineering'?

This book is designed for software engineers, data scientists, and AI practitioners who are looking to deepen their understanding of machine learning from an engineering perspective. It's also suitable for advanced students who wish to gain practical insights into developing and deploying machine learning models.

What programming languages does 'Machine Learning Engineering' focus on?

The book primarily focuses on Python and its extensive machine learning libraries such as TensorFlow and PyTorch. Python is widely used in the industry, making this book relevant for practitioners aiming to implement machine learning solutions in real-world applications.

Does 'Machine Learning Engineering' include practical examples?

Yes, the book includes numerous practical examples and case studies to illustrate how theoretical concepts are applied in real-world scenarios. These examples enhance understanding and provide actionable insights for readers aspiring to excel in machine learning engineering.

Can beginners benefit from 'Machine Learning Engineering'?

While this book is mainly aimed at those with some technical background, motivated beginners with a basic understanding of programming and statistics can gain valuable insights. The clear explanations and practical examples will help them gradually build their knowledge and skills in machine learning.

What makes 'Machine Learning Engineering' different from other machine learning books?

This book uniquely focuses on the engineering aspects of machine learning projects, emphasizing the entire lifecycle from conception to deployment. Unlike many texts that dwell solely on algorithms, it integrates software engineering principles, making it essential for practitioners who want to create scalable and maintainable solutions.

How does 'Machine Learning Engineering' approach model evaluation?

The book provides a comprehensive approach to model evaluation by discussing various metrics and methodologies to assess model performance. This includes detailed explanations of concepts like precision, recall, F1-score, and ROC curves, empowering readers to make informed decisions about model selection and improvement.

Are there any prerequisites to read 'Machine Learning Engineering'?

Having a foundational knowledge of programming—particularly in Python—and an understanding of basic statistics will enhance the reading experience. Familiarity with concepts in machine learning is beneficial but not strictly necessary, as the book aims to guide readers through essential principles if they put in the effort.

How does 'Machine Learning Engineering' handle real-world challenges in machine learning?

The book addresses common challenges such as data quality, model drift, and scalability concerns. By providing insights on how to overcome these challenges, it prepares readers for the complexities of implementing successful machine learning systems in dynamic and unpredictable environments.

Where can I buy 'Machine Learning Engineering' in Norway?

'Machine Learning Engineering' can be purchased through Ubuy, a reliable e-commerce platform that offers a wide range of products. Ubuy provides an intuitive shopping experience for customers in Norway, ensuring easy access to this insightful book.

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Is Machine Learning Engineering Available to Shop Online in Norway?

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This book is one of the few to offer perspectives on each step of the end-to-end process.

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Machine Learning Engineering

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Item #: 28838529

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What Stands Out

Comprehensive Coverage

This book offers in-depth insights into machine learning engineering, covering essential concepts and practical applications, making it suitable for both beginners and experienced professionals.

Real-World Examples

Features practical case studies and examples that demonstrate the application of machine learning techniques, helping readers understand complex ideas in a tangible way.

Up-to-Date Content

Published in 2020, it includes the latest advancements in the field, ensuring readers are well-versed with current trends, tools, and technologies in machine learning.

Product Details

Written by Andriy Burkov, author of The Hundred-Page Machine Learning Book
Based on the author's 15 years of AI experience and industry leaders' insights
Addresses best practices and design patterns for building scalable machine learning solutions
Includes perspectives on decision-making, product management, data engineering, statistics, reliability engineering, and more
Embraces the importance of learning from mistakes and provides guidance on preventing, detecting, and handling issues in practical machine learning
Ideal for those seeking to use machine learning to solve business problems at scale

Publisher	True Positive Inc.
Publication date	September 5, 2020
Language	English
Print length	310 pages
ISBN-10	1999579577
ISBN-13	978-1999579579
Item Weight	1.3 pounds (590 grams)
Dimensions	7.5 x 0.73 x 9.25 inches (19.1 x 1.9 x 23.5 cm)

Who Should Buy?

Aspiring Engineers
Ideal for those beginning their journey in machine learning engineering, providing foundational knowledge and practical application guidance.
Data Scientists
Perfect for data scientists looking to expand their skill set and apply machine learning techniques in engineering projects.
Tech Professionals
Beneficial for software engineers and IT professionals seeking to integrate machine learning strategies into their existing workflows.

Beginners Only
Not suitable for those with no technical background or prior understanding of programming and data science concepts.

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Machine Learning Engineering

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Question: How to Shop Machine Learning Engineering Online From Ubuy?

Answer: It’s easy to shop Machine Learning Engineering online from Ubuy. You just have to search for the product, choose your shipping method while checking out and get it delivered to your location.
Question: Is Machine Learning Engineering Available to Shop Online in Norway?

Answer: Yes, at Ubuy Norway this product is available for you to shop at a reasonable price. The Machine Learning Engineering is not available locally but you can trust us with our express shipping services.
Question: How Long Does It Take to Get Product After Placing the Order?

Answer: The delivery time of your ordered product varies as per what you've ordered and the shipping method that you've chosen. The estimated delivery time is mentioned during the checkout process, so be carefree while shopping.

Expert Systems Editorial Review

Machine Learning Engineering is a concise book that covers various topics related to machine learning. While it may not be thorough on all subjects, it provides a brief overview of techniques. It is a useful resource for quick reference and preparation before interviews. However, some readers have mentioned missing pages and the lack of a preface and table of contents. The book offers practical examples and guidance on using machine learning. Overall, it is well-received for its depth and breadth of topics, although it may be redundant for those who have read the author's previous book. It is recommended for individuals seeking a deeper understanding of machine learning engineering and MLOps.

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Pros

Concise and captures the depth and breadth of topics
Useful for quick reference and interview preparation
Provides practical guidance on utilizing machine learning
Covers various topics in machine learning engineering comprehensively

Cons

Missing pages and lack of preface and table of contents

Product Price History

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Features & Benefits

Most complete applied AI book out there
Filled with best practices and design patterns
Written by an industry leader with 15 years of experience
Covers all aspects of building reliable ML solutions
Emphasizes the importance of monitoring and model maintenance
A must-have for anyone working in machine learning

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Machine Learning Engineering

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