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 by Valliappa Lakshmanan, Sara Robinson and Michael Munn - Beijng : O'Reilly, ©2022 - xiv, 390 p. : ill. ; 23 cm.
Includes index.
The need for machine learning design patterns -- Data representation design patterns -- Problem representation design patterns -- Model training patterns -- Design patterns for resilient serving -- Reproducibility design patterns -- Responsible AI -- Connected patterns.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 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 best technique for your situation.--
9789385889219
2021443780
Machine learning.
Big data.
Big data.
Machine learning.
006.31 / LAK-M
Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps by Valliappa Lakshmanan, Sara Robinson and Michael Munn - Beijng : O'Reilly, ©2022 - xiv, 390 p. : ill. ; 23 cm.
Includes index.
The need for machine learning design patterns -- Data representation design patterns -- Problem representation design patterns -- Model training patterns -- Design patterns for resilient serving -- Reproducibility design patterns -- Responsible AI -- Connected patterns.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 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 best technique for your situation.--
9789385889219
2021443780
Machine learning.
Big data.
Big data.
Machine learning.
006.31 / LAK-M