BOOKS - Machine Learning Design Patterns: Solutions to Common Challenges in Data Prep...
US $6.80
626722
626722
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Author: Valliappa Lakshmanan
Year: November 3, 2020
Format: PDF
File size: PDF 19 MB
Language: English
Year: November 3, 2020
Format: PDF
File size: PDF 19 MB
Language: English
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 modelsRepresent data for different ML model types, including embeddings, feature crosses, and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure that models are treating users fairly
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