Preface

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

Automated Machine Learning on AWS begins with a quick overview of what the ML pipeline/process looks like and highlights the typical challenges you may face when building a pipeline. By reading the book, you'll become well versed in various AWS solutions, such as Amazon SageMaker Autopilot, AutoGluon, AWS Step Functions, and more, and will learn how 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 Amazon Managed Services for Apache Airflow and will build a managed Airflow environment. You'll also cover the key success criteria for an Machine Learning Software Development Life Cycle (MLSDLC) implementation and the process of creating a self-mutating CI/CD pipeline using the CDK from the perspective of the platform engineering team.

By the end of the book, you'll be able to effectively automate a complete ML pipeline and deploy it to production.