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Cloud Native AI and machine learning on AWS

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Cloud Native AI and machine learning on AWS/ Premkumar Rangarajan, David Bounds.
Author:	Rangarajan, Premkumar.
other author:	Bounds, David,
Description:	1 online resource (401 pages)
Subject:	COMPUTERS / Data Science / Machine Learning. -
Online resource:	https://portal.igpublish.com/iglibrary/search/BPB0000403.html
ISBN:	9789355513267

Cloud Native AI and machine learning on AWS
Rangarajan, Premkumar.

Cloud Native AI and machine learning on AWS[electronic resource] /Premkumar Rangarajan, David Bounds. - 1 online resource (401 pages)

Includes bibliographical references and index.

Cloud Native AI and machine learning on AWS -- About the Authors -- About the Reviewers -- Acknowledgement -- Preface -- Errata -- Table of Contents -- Chapter 1 Introducing the ML Workflow -- Chapter 2 Hydrating the Data Lake -- Chapter 3 Predicting the Future With Features -- Chapter 4 Orchestrating the Data Continuum -- Chapter 5 Casting a Deeper Net (Algorithms and Neural Networks) -- Chapter 6 Iteration Makes Intelligence (Model Training and Tuning) -- Chapter 7 Let George Take Over (AutoML in Action) -- Chapter 8 Blue or Green (Model Deployment Strategies) -- Chapter 9 Wisdom at Scale with Elastic Inference -- Chapter 10 Adding Intelligence with Sensory Cognition -- Chapter 11 AI for Industrial Automation -- Chapter 12 Operationalized Model Assembly (MLOps and Best Practices) -- Index.

Access restricted to authorized users and institutions.

Using machine learning and artificial intelligence (AI) in existing business processes has been successful. Even AWS's ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation. In this book, you'll learn how to build data lakes, build and train machine learning models, automate MLOps, ensure maximum data reusability and reproducibility, and much more. The applications presented in the book show how to make the most of several different AWS offerings, including Amazon Comprehend, Amazon Rekognition, Amazon Lookout, and AutoML. This book teaches you to manage massive data lakes, train artificial intelligence models, release these applications into production, and track their progress in real-time. You will learn how to use the pre-trained models for various tasks, including picture recognition, automated data extraction, image/video detection, and anomaly detection. Every step of your Machine Learning and AI project's development process is optimised throughout the book by utilising Amazon's pre-made, purpose-built AI services.

Mode of access: World Wide Web.

ISBN: 9789355513267Subjects--Topical Terms:

1483854
COMPUTERS / Data Science / Machine Learning.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA76.9

Dewey Class. No.: 004

Cloud Native AI and machine learning on AWS
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100 1 $a Rangarajan, Premkumar. $3 1483890
245 1 0 $a Cloud Native AI and machine learning on AWS $h [electronic resource] / $c Premkumar Rangarajan, David Bounds.
264 1 $a [Place of publication not identified] : $b BPB Publications, $c 2023.
264 4 $c ©2023
300 $a 1 online resource (401 pages)
336 $a text $b txt $2 rdacontent
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338 $a online resource $b cr $2 rdacarrier
504 $a Includes bibliographical references and index.
505 0 $a Cloud Native AI and machine learning on AWS -- About the Authors -- About the Reviewers -- Acknowledgement -- Preface -- Errata -- Table of Contents -- Chapter 1 Introducing the ML Workflow -- Chapter 2 Hydrating the Data Lake -- Chapter 3 Predicting the Future With Features -- Chapter 4 Orchestrating the Data Continuum -- Chapter 5 Casting a Deeper Net (Algorithms and Neural Networks) -- Chapter 6 Iteration Makes Intelligence (Model Training and Tuning) -- Chapter 7 Let George Take Over (AutoML in Action) -- Chapter 8 Blue or Green (Model Deployment Strategies) -- Chapter 9 Wisdom at Scale with Elastic Inference -- Chapter 10 Adding Intelligence with Sensory Cognition -- Chapter 11 AI for Industrial Automation -- Chapter 12 Operationalized Model Assembly (MLOps and Best Practices) -- Index.
506 $a Access restricted to authorized users and institutions.
520 3 $a Using machine learning and artificial intelligence (AI) in existing business processes has been successful. Even AWS's ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation. In this book, you'll learn how to build data lakes, build and train machine learning models, automate MLOps, ensure maximum data reusability and reproducibility, and much more. The applications presented in the book show how to make the most of several different AWS offerings, including Amazon Comprehend, Amazon Rekognition, Amazon Lookout, and AutoML. This book teaches you to manage massive data lakes, train artificial intelligence models, release these applications into production, and track their progress in real-time. You will learn how to use the pre-trained models for various tasks, including picture recognition, automated data extraction, image/video detection, and anomaly detection. Every step of your Machine Learning and AI project's development process is optimised throughout the book by utilising Amazon's pre-made, purpose-built AI services.
538 $a Mode of access: World Wide Web.
650 7 $a COMPUTERS / Data Science / Machine Learning. $2 bisacsh $3 1483854
650 7 $a COMPUTERS / Distributed Systems / Cloud Computing. $2 bisacsh $3 1483893
650 7 $a COMPUTERS / Languages / Python. $2 bisacsh $3 1483892
650 7 $a COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition. $2 bisacsh $3 1483850
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Bounds, David, $e author $3 1483891
856 4 0 $u https://portal.igpublish.com/iglibrary/search/BPB0000403.html