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.
  type of resource: text
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: cau
    date issued: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  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: Himanshu Singh.
  note: Includes index.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  subject:
    name:
      name part: Amazon Web Services (Firm)
  subject:
    name:
      name part: Amazon Web Services (Firm)
  subject:
    topic: Machine learning
  subject:
    topic: Big data
  subject:
    topic: Application software
  subject:
    topic: Open source software
  subject:
    topic: Computer programming
  identifier: 9781484262221
  identifier: 1484262220
  identifier: 9781484262214
  identifier: 1484262212
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: UPM
    record creation date: 201124
    record change date: 20240717213016.0
    record identifier: kn-on1228845789
    language of cataloging:
      language term: eng