國立虎尾科技大學 |

Practical Machine Learning with AWS = Process, Build, Deploy, and Productionize Your Models Using AWS /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Practical Machine Learning with AWS / by Himanshu Singh.
Reminder of title:	Process, Build, Deploy, and Productionize Your Models Using AWS /
Author:	Singh, Himanshu.
Description:	XVII, 241 p. 128 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-6222-1
ISBN:	9781484262221

Practical Machine Learning with AWS = Process, Build, Deploy, and Productionize Your Models Using AWS /
Singh, Himanshu.

Practical Machine Learning with AWS Process, Build, Deploy, and Productionize Your Models Using AWS /[electronic resource] :by Himanshu Singh. - 1st ed. 2021. - XVII, 241 p. 128 illus.online resource.

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

Standard No.: 10.1007/978-1-4842-6222-1doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Practical Machine Learning with AWS = Process, Build, Deploy, and Productionize Your Models Using AWS /
LDR:03669nam a22003975i 4500 001 1049217
003 DE-He213
005 20210622071909.0
007 cr nn 008mamaa
008 220103s2021 xxu| s |||| 0|eng d
020 $a 9781484262221 $9 978-1-4842-6222-1
024 7 $a 10.1007/978-1-4842-6222-1 $2 doi
035 $a 978-1-4842-6222-1
050 4 $a Q325.5-.7
050 4 $a TK7882.P3
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
100 1 $a Singh, Himanshu. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1303798
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.
250 $a 1st ed. 2021.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a XVII, 241 p. 128 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
650 0 $a Machine learning. $3 561253
650 0 $a Big data. $3 981821
650 0 $a Application software. $3 528147
650 0 $a Open source software. $3 561177
650 0 $a Computer programming. $3 527822
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Computer Applications. $3 669785
650 2 4 $a Open Source. $3 1113081
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484262214
776 0 8 $i Printed edition: $z 9781484262238
856 4 0 $u https://doi.org/10.1007/978-1-4842-6222-1
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)