Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps

By:

Lakshmanan, Valliappa

Contributor(s):

Material type: Text

9789385889219

Subject(s):

DDC classification:

006.31 LAK-M

Contents:

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.

Summary: 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.--

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Books	IIITD General Stacks	Computer Science and Engineering	006.31 LAK-M (Browse shelf(Opens below))	Available		012939

Total holds: 0

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Previous	006.31 KEL-D Deep learning	006.31 KEL-M Machine learning in production :	006.31 KIR-T Thoughtful machine learning: a test-driven approach	006.31 LAK-M Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps	006.31 MIC-M Machine learning, neural and statistical classification	006.31 MIT-M Machine learning	006.31 MIT-M Machine learning	Next

Includes index.

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.--

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