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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.
In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.
You’ll learn how to:
Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
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Machine Learning, AI & more
Machine Learning
Artificial Intelligence
Deep Learning
Language Processing (NLP, LLM)
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Publisher : O’Reilly Media; 1st edition (November 24, 2020)
Language : English
Paperback : 405 pages
ISBN-10 : 1098115783
ISBN-13 : 978-1098115784
Item Weight : 2.31 pounds
Dimensions : 9.06 x 0.94 x 6.85 inches
Customers say
Customers find the book useful and interesting for machine learning practitioners of all skill levels. They appreciate the well-written examples and good ideas presented in the design patterns section. The book provides a solid resource for applying machine learning theory to practical applications.
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