Automated Machine Learning on AWS

With "Automated Machine Learning on AWS," you'll learn how to streamline the development and deployment of machine learning workflows using the AWS ecosystem. This comprehensive guide equips you to leverage AWS services such as SageMaker, Step Functions, and more to simplify pipeline automation and tackle ML challenges with confidence and efficiency.

What this Book will help me do

• Understand how to use AWS SageMaker Autopilot to streamline machine learning pipeline development.
• Apply AutoGluon for automating complex machine learning model creation.
• Build and manage robust CI/CD pipelines tailored for machine learning use cases on AWS.
• Learn to implement data-centric machine learning workflows using AWS MWAA.
• Leverage tools like AWS Step Functions and CDK to create production-ready automated ML workflows.

Author(s)

Trenton Potgieter is a seasoned professional in the fields of machine learning and cloud computing, with extensive experience in leveraging AWS to develop automated workflows. Holding certifications in AWS and a passion for applying technology to real-world problems, his approach blends technical expertise with practical application. Through his writing, Trenton aims to make complex topics accessible and actionable for all readers.

Who is it for?

This book is ideal for both beginner and experienced machine learning practitioners who are seeking ways to implement automated solutions for model development, training, and deployment on AWS. To fully benefit from this book, you should have a foundational knowledge of the machine learning process, some Python programming skills, and familiarity with AWS services.