語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
到查詢結果
[ subject:"Amazon Web Services (Firm)" ]
切換:
標籤
|
MARC模式
|
ISBD
Practical machine learning with AWS ...
~
Singh, Himanshu.
FindBook
Google Book
Amazon
博客來
Practical machine learning with AWS = process, build, deploy, and productionize your models using AWS /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Practical machine learning with AWS/ by Himanshu Singh.
其他題名:
process, build, deploy, and productionize your models using AWS /
作者:
Singh, Himanshu.
出版者:
Berkeley, CA :Apress : : 2021.,
面頁冊數:
xvii, 241 p. :ill., digital ;24 cm.
內容註:
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.
Contained By:
Springer Nature eBook
標題:
Machine learning. -
電子資源:
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. - 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
Standard No.: 10.1007/978-1-4842-6222-1doiSubjects--Corporate Names:
3242822
Amazon Web Services (Firm)
Subjects--Topical Terms:
533906
Machine learning.
LC Class. No.: Q325.5 / .S55 2021
Dewey Class. No.: 006.31
Practical machine learning with AWS = process, build, deploy, and productionize your models using AWS /
LDR
:03349nmm a2200325 a 4500
001
2236631
003
DE-He213
005
20201124024546.0
006
m d
007
cr nn 008maaau
008
211111s2021 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
3385890
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
3242822
650
0
$a
Machine learning.
$3
533906
650
0
$a
Big data.
$3
2045508
650
0
$a
Application software.
$3
527258
650
0
$a
Open source software.
$3
581998
650
0
$a
Computer programming.
$3
527209
650
1 4
$a
Machine Learning.
$3
3382522
650
2 4
$a
Big Data.
$3
3134868
650
2 4
$a
Computer Applications.
$3
891249
650
2 4
$a
Open Source.
$3
2210577
710
2
$a
SpringerLink (Online service)
$3
836513
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)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9398516
電子資源
11.線上閱覽_V
電子書
EB Q325.5 .S55 2021
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入