Read [pdf]> Machine Learning Design Patterns:

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

• 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

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

Ebooks for mobile download Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 9781098115784 PDB by Valliappa Lakshmanan, Sara Robinson, Michael Munn in English

Overview

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

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