000			02146 a2200253 4500
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020			_a9789385889219
082			_a006.31 LAK
100			_aLakshmanan, Valliappa
245			_aMachine learning design patterns: solutions to common challenges in data preparation, model building, and MLOps
260			_aMumbai: _bShroff Publisher & O'Reilly, _c2022.
300			_axiv, 390p.: _bill.; pbk.: _c23cm.
504			_aIncludes index.
520			_aThe 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 https://www.shroffpublishers.com/books/computer-science/artificial-intelligence/machine-learning/9789385889219/
650			_aMachine Learning
650			_aBig Data
650			_aModel Training Patterns
650			_aProblem Representation Design Pattern
650			_aResponsible AI
650			_aConnected Patterns
700			_aRobinson, Sara _eCo-author
700			_aMunn, Michael _eCo-author
942			_cTD _2ddc
999			_c61130 _d61130