Machine learning design patterns: solutions to common challenges in data preparation, model building, and MLOps (Record no. 61130)

MARC details
000 -LEADER
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789385889219
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 LAK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Lakshmanan, Valliappa
245 ## - TITLE STATEMENT
Title Machine learning design patterns: solutions to common challenges in data preparation, model building, and MLOps
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Mumbai:
Name of publisher, distributor, etc Shroff Publisher & O'Reilly,
Date of publication, distribution, etc 2022.
300 ## - PHYSICAL DESCRIPTION
Extent xiv, 390p.:
Other physical details ill.; pbk.:
Dimensions 23cm.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes index.
520 ## - SUMMARY, ETC.
Summary, etc 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. <br/><br/>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. <br/><br/>You'll learn how to:<br/><br/>Identify and mitigate common challenges when training, evaluating, and deploying ML models <br/>Represent data for different ML model types, including embeddings, feature crosses, and more <br/>Choose the right model type for specific problems <br/>Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning <br/>Deploy scalable ML systems that you can retrain and update to reflect new data<br/><br/>https://www.shroffpublishers.com/books/computer-science/artificial-intelligence/machine-learning/9789385889219/
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big Data
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Model Training Patterns
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Problem Representation Design Pattern
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Responsible AI
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Connected Patterns
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Robinson, Sara
Relator term Co-author
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Munn, Michael
Relator term Co-author
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Books
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Date last borrowed Copy number Cost, replacement price Koha item type
    Dewey Decimal Classification     General IIT Gandhinagar IIT Gandhinagar 09/10/2024 Shankar Books 1600.00 5 006.31 LAK 034601 13/10/2025 26/09/2025 1 1600.00 Books


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