Machine learning design patterns: solutions to common challenges in data preparation, model building, and MLOps
Lakshmanan, Valliappa
Machine learning design patterns: solutions to common challenges in data preparation, model building, and MLOps - Mumbai: Shroff Publisher & O'Reilly, 2022. - xiv, 390p.: ill.; pbk.: 23cm.
Includes index.
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
https://www.shroffpublishers.com/books/computer-science/artificial-intelligence/machine-learning/9789385889219/
9789385889219
Machine Learning
Big Data
Model Training Patterns
Problem Representation Design Pattern
Responsible AI
Connected Patterns
006.31 LAK
Machine learning design patterns: solutions to common challenges in data preparation, model building, and MLOps - Mumbai: Shroff Publisher & O'Reilly, 2022. - xiv, 390p.: ill.; pbk.: 23cm.
Includes index.
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
https://www.shroffpublishers.com/books/computer-science/artificial-intelligence/machine-learning/9789385889219/
9789385889219
Machine Learning
Big Data
Model Training Patterns
Problem Representation Design Pattern
Responsible AI
Connected Patterns
006.31 LAK
