Practical Machine Learning with AWS

Practical Machine Learning with AWS: Process, Build, Deploy, and Productionize Your Models Using AWS

By Himanshu Singh

Practical Machine Learning with AWS is an end-to-end, hands-on guide to building, training, deploying, and productionizing machine learning models using Amazon Web Services. Whether you’re a machine learning engineer, data scientist, or cloud developer, this book teaches you how to harness the power of AWS services to turn raw data into real-world, scalable ML applications.

The book is organized into three main parts:

📌 Part I – AWS Foundations:
Learn essential AWS infrastructure and services such as Amazon S3, EC2, Identity and Access Management (IAM), CloudFormation, and CloudWatch. This section also covers cloud security fundamentals to give you a solid foundation for ML workflows in AWS.

📌 Part II – Machine Learning with AWS:
Dive deep into AWS SageMaker, the primary ML service that allows you to build, train, tune, and deploy models at scale. You’ll explore how to set up data pipelines, execute training jobs, monitor model performance, and automate deployment.

📌 Part III – Other AWS ML Services:
Discover additional services such as Amazon Comprehend for natural language processing, Amazon Forecast for time series forecasting, and Amazon Textract for extracting data from documents — broadening your understanding of AWS’s ML ecosystem.

By the end of the book, you’ll not only understand the full machine learning pipeline on AWS, but also how to apply these tools in live projects and prepare for the AWS Certified Machine Learning – Specialty exam.

Who this book is for:
Perfect for ML engineers, cloud professionals, and data scientists who want a practical, project-driven approach to machine learning on AWS and real deployment experience — from data preprocessing through production-ready solutions.

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover