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

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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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Singh, Himanshu (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Berkeley, CA] : Apress, [2021]
Temas:
Amazon Web Services (Firm)
Machine learning.
Big data.
Application software.
Open source software.
Computer programming.
Apprentissage automatique.
Données volumineuses.
Logiciels d'application.
Logiciels libres.
Programmation (Informatique)
computer programming.
Application software
Big data
Computer programming
Machine learning
Open source software
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.-

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