Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Design patterns in this book provide best practices and solutions to recurring problems in machine learning, codified by three Google engineers. It offers 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness, with detailed explanations and recommendations for choosing the best technique.

\n Format: Paperback / softback
\n Length: 400 pages
\n Publication date: 27 October 2020
\n Publisher: O'Reilly Media, Inc, USA
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This book is a valuable resource for data scientists looking to improve their skills in machine learning. The authors, three Google engineers, have compiled a comprehensive collection of proven methods and design patterns that can help data scientists tackle common problems throughout the ML process.

The design patterns presented in this book are based on the experience of hundreds of experts in the field, and they are designed to be straightforward and approachable. Each pattern includes a description of the problem it addresses, a variety of potential solutions, and recommendations for choosing the best technique for a particular situation.

The book covers a wide range of topics, including data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. It provides detailed explanations of 30 patterns for these areas, each of which includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique.

One of the key strengths of this book is its emphasis on practical applications. The authors provide real-world examples and case studies that demonstrate how the design patterns can be applied to different ML problems. This helps readers to understand the practical implications of the patterns and to apply them in their own work.

Another notable feature of the book is its comprehensive coverage of ML model types. It provides detailed explanations of embeddings, feature crosses, and other common ML model types, and it helps readers to choose the right model type for their specific problem. The book also includes guidance on building a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning, which are essential components of any ML project.

In conclusion, this book is a must-read for data scientists looking to improve their skills in machine learning. It provides a comprehensive collection of proven methods and design patterns that can help data scientists tackle common problems throughout the ML process. The authors' practical approach and emphasis on practical applications make this book an invaluable resource for anyone working in the field.

\n Weight: 698g\n
Dimension: 177 x 233 x 25 (mm)\n
ISBN-13: 9781098115784\n \n