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Machine Learning Design Patterns : Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Sara Robinson, Valliappa Lakshmanan and Michael Munn (2020, Trade Paperback)

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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated
ISBN-101098115783
ISBN-139781098115784
eBay Product ID (ePID)25050068166

Product Key Features

Number of Pages405 Pages
Publication NameMachine Learning Design Patterns : Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
LanguageEnglish
SubjectEnterprise Applications / Business Intelligence Tools, Intelligence (Ai) & Semantics, General
Publication Year2020
TypeTextbook
AuthorSara Robinson, Valliappa Lakshmanan, Michael Munn
Subject AreaComputers, Science
FormatTrade Paperback

Dimensions

Item Height0.8 in
Item Weight24.6 Oz
Item Length9.3 in
Item Width7.8 in

Additional Product Features

Intended AudienceScholarly & Professional
LCCN2021-443780
Dewey Edition23
IllustratedYes
Dewey Decimal006.31
SynopsisThe 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. 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 Interpret model predictions for stakeholders and ensure models are treating users fairly
LC Classification NumberQ325.5

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