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

By: Contributor(s): Material type: TextTextPublication details: Beijng : O'Reilly, ©2022Description: xiv, 390 p. : ill. ; 23 cmISBN:
  • 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 Books IIITD General Stacks Computer Science and Engineering 006.31 LAK-M (Browse shelf(Opens below)) Available 012939
Total holds: 0

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.--

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