Practical machine learning with AWS : process, build, deploy, and productionize your models using AWS / Himanshu Singh.
Singh, Himanshu, author.
2021
Q325.5
Available Online

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Practical machine learning with AWS : process, build, deploy, and productionize your models using AWS / Himanshu Singh.
Author
Singh, Himanshu, author.
ISBN
9781484262221 (electronic bk.)
1484262220 (electronic bk.)
9781484262214
1484262212
DOI
https://doi.org/10.1007/978-1-4842-6222-1
Published
[Berkeley, CA] : Apress, [2021]
Language
English
Description
1 online resource
Item Number
10.1007/978-1-4842-6222-1 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
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.
Note
Includes index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 10, 2021).
Available in Other Form
Print version: 9781484262214
Print version: 9781484262238
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
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.-

Browse Subjects

Show more subjects...

Statistics

from
to
Export