Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn

Free ebooks in pdf files to download Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

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

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Valliappa Lakshmanan, Sara Robinson, Michael Munn
Page: 400
Format: pdf, ePub, mobi, fb2
ISBN: 9781098115784
Publisher: O'Reilly Media, Incorporated

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

Free ebooks in pdf files to download Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow. The authors, three Google Cloud engineers, describe 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 most appropriate remedy 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 that models are treating users fairly

Lakshmanan Valliappa, Robinson Sara, Munn Michael
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