Automated Machine Learning on AWS

Automated Machine Learning on AWS

eBook Details:

  • Paperback: 420 pages
  • Publisher: WOW! eBook (April 15, 2022)
  • Language: English
  • ISBN-10: 1801811822
  • ISBN-13: 978-1801811828

eBook Description:

Automated Machine Learning on AWS: Automate the process of building, training, and deploying machine learning applications to production with AWS solutions such as SageMaker Autopilot, AutoGluon, Step Functions, Amazon Managed Workflows for Apache Airflow, and more

AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you’ll learn how to automate a machine learning pipeline using the various AWS services.

Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you’ll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You’ll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed Airflow environment. You’ll also cover the key success criteria for an MLSDLC implementation and the process of creating a self-mutating CI/CD pipeline using AWS CDK from the perspective of the platform engineering team.

  • Employ SageMaker Autopilot and Amazon SageMaker SDK to automate the machine learning process
  • Understand how to use AutoGluon to automate complicated model building tasks
  • Use the AWS CDK to codify the machine learning process
  • Create, deploy, and rebuild a CI/CD pipeline on AWS
  • Build an ML workflow using AWS Step Functions and the Data Science SDK
  • Leverage the Amazon SageMaker Feature Store to automate the machine learning software development life cycle (MLSDLC)
  • Discover how to use Amazon MWAA for a data-centric ML process

By the end of this Automated Machine Learning on AWS book, you’ll be able to effectively automate a complete machine learning pipeline and deploy it to production.

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