Practical machine learning with AWS : process, build, deploy, and productionize your models using AWS

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 A...

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Bibliographic Details
Main Author: Singh, Himanshu, (Author)
Format: eBook
Language: English
Published: [Berkeley, CA] : Apress, [2021]
Subjects:
Amazon Web Services (Firm)
Machine learning.
Big data.
Application software.
Open source software.
Computer programming.
elektronické knihy
electronic books
ISBN: 9781484262221
1484262220
9781484262214
1484262212
Physical Description: 1 online resource

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Table of contents

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020 |a 9781484262221  |q (electronic bk.) 
020 |a 1484262220  |q (electronic bk.) 
020 |z 9781484262214 
020 |z 1484262212 
024 7 |a 10.1007/978-1-4842-6222-1  |2 doi 
035 |a (OCoLC)1228845789  |z (OCoLC)1224199289  |z (OCoLC)1224366418  |z (OCoLC)1238204339  |z (OCoLC)1249945056 
100 1 |a Singh, Himanshu,  |e author. 
245 1 0 |a Practical machine learning with AWS :  |b process, build, deploy, and productionize your models using AWS /  |c Himanshu Singh. 
264 1 |a [Berkeley, CA] :  |b Apress,  |c [2021] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a Includes index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
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. 
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.- 
590 |a Knovel  |b Knovel (All titles) 
610 2 0 |a Amazon Web Services (Firm) 
610 2 7 |a Amazon Web Services (Firm)  |2 fast 
650 0 |a Machine learning. 
650 0 |a Big data. 
650 0 |a Application software. 
650 0 |a Open source software. 
650 0 |a Computer programming. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |z 9781484262214 
776 0 8 |i Print version:  |z 9781484262238 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpPMLAWSP2/practical-machine-learning?kpromoter=marc  |y Full text 

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