國立高雄大學圖資館 |

Language: English

Back to Search results for [ author_sort:"singh, himanshu." ]

Practical machine learning with AWSp...

Amazon Web Services (Firm)

Practical machine learning with AWSprocess, build, deploy, and productionize your models using AWS /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical machine learning with AWSby Himanshu Singh.
Reminder of title:	process, build, deploy, and productionize your models using AWS /
Author:	Singh, Himanshu.
Published:	Berkeley, CA :Apress :2021.
Description:	xvii, 241 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-6222-1
ISBN:	9781484262221$q(electronic bk.)

Practical machine learning with AWSprocess, build, deploy, and productionize your models using AWS /
Singh, Himanshu.

Practical machine learning with AWSprocess, build, deploy, and productionize your models using AWS /[electronic resource] :by Himanshu Singh. - Berkeley, CA :Apress :2021. - xvii, 241 p. :ill., digital ;24 cm.

Part I: Introduction to Amazon Web Services -- Chapter 1: Cloud Computing and AWS -- Chapter 2: AWS Pricing and Cost Management -- Chapter 3: Security in Amazon Web Services -- Part II: Machine Learning in AWS -- Chapter 4: Introduction to Machine Learning -- Chapter 5: Data Processing in AWS -- Chapter 6: Building and Deploying Models in SageMaker -- Chapter 7: Using CloudWatch in SageMaker -- Chapter 8: Running a Custom Algorithm in SageMaker -- Chapter 9: Making an End-to-End Pipeline in SageMaker -- Part III: Other AWS Services -- Chapter 10: Machine Learning Use Cases in AWS -- Appendix A: Creating a Root User Account to Access Amazon Management Console -- Appendix B: Creating an IAM Role -- Appendix C: Creating an IAM User- Appendix D: Creating an S3 Bucket -- Appendix E: Creating a SageMaker Notebook Instance.

Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract. By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning-Specialty certification exam. You will: Be familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud Formation Understand SageMaker, Amazon Comprehend, and Amazon Forecast Execute live projects: from the pre-processing phase to deployment on AWS.

ISBN: 9781484262221$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6222-1doiSubjects--Corporate Names:

785206
Amazon Web Services (Firm)
Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .S55 2021

Dewey Class. No.: 006.31

Practical machine learning with AWSprocess, build, deploy, and productionize your models using AWS /
LDR:03349nmm a2200325 a 4500 001 596171
003 DE-He213
005 20201124024546.0
006 m d
007 cr nn 008maaau
008 211013s2021 cau s 0 eng d
020 $a 9781484262221$q(electronic bk.)
020 $a 9781484262214$q(paper)
024 7 $a 10.1007/978-1-4842-6222-1 $2 doi
035 $a 978-1-4842-6222-1
040 $a GP $c GP $e rda
041 0 $a eng
050 4 $a Q325.5 $b .S55 2021
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S617 2021
100 1 $a Singh, Himanshu. $3 835577
245 1 0 $a Practical machine learning with AWS $h [electronic resource] : $b process, build, deploy, and productionize your models using AWS / $c by Himanshu Singh.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xvii, 241 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part I: Introduction to Amazon Web Services -- Chapter 1: Cloud Computing and AWS -- Chapter 2: AWS Pricing and Cost Management -- Chapter 3: Security in Amazon Web Services -- Part II: Machine Learning in AWS -- Chapter 4: Introduction to Machine Learning -- Chapter 5: Data Processing in AWS -- Chapter 6: Building and Deploying Models in SageMaker -- Chapter 7: Using CloudWatch in SageMaker -- Chapter 8: Running a Custom Algorithm in SageMaker -- Chapter 9: Making an End-to-End Pipeline in SageMaker -- Part III: Other AWS Services -- Chapter 10: Machine Learning Use Cases in AWS -- Appendix A: Creating a Root User Account to Access Amazon Management Console -- Appendix B: Creating an IAM Role -- Appendix C: Creating an IAM User- Appendix D: Creating an S3 Bucket -- Appendix E: Creating a SageMaker Notebook Instance.
520 $a Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract. By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning-Specialty certification exam. You will: Be familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud Formation Understand SageMaker, Amazon Comprehend, and Amazon Forecast Execute live projects: from the pre-processing phase to deployment on AWS.
610 2 0 $a Amazon Web Services (Firm) $3 785206
650 0 $a Machine learning. $3 188639
650 0 $a Big data. $3 609582
650 0 $a Application software. $3 200645
650 0 $a Open source software. $3 200208
650 0 $a Computer programming. $3 181992
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Big Data. $3 760530
650 2 4 $a Computer Applications. $3 273760
650 2 4 $a Open Source. $3 758930
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6222-1
950 $a Professional and Applied Computing (SpringerNature-12059)