rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Practical Machine Learning with AWS
    sub title: Process, Build, Deploy, and Productionize Your Models Using AWS
  name:
    name part: Singh, Himanshu.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xxu
    date issued: 2021
    edition: 1st ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XVII, 241 pages 128 illustrations)
  abstract: 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.
  table of contents: 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.-.
  note: by Himanshu Singh.
  subject:
    topic: Machine learning
  subject:
    topic: Big data
  subject:
    topic: Application software
  subject:
    topic: Open source software
  subject:
    topic: Computer programming
  subject:
    topic: Machine Learning
  subject:
    topic: Big Data
  subject:
    topic: Computer Applications
  subject:
    topic: Open Source
  classification: Q325.5-.7
  classification: TK7882.P3
  classification: 006.31
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9781484262221
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 201124
    record change date: 20230330185126.2
    record identifier: in00004390358