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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
From the brand
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
Publication date : November 24, 2020
Edition : 1st
Language : English
Print length : 405 pages
ISBN-10 : 1098115783
ISBN-13 : 978-1098115784
Item Weight : 2.31 pounds
Dimensions : 9.06 x 0.94 x 6.85 inches
Best Sellers Rank: #54,796 in Books (See Top 100 in Books) #10 in Business Intelligence Tools #10 in Machine Theory (Books) #140 in Artificial Intelligence & Semantics
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