Preface

The AWS Machine Learning Specialty certification exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus in depth using practical examples to help you with your real-world ML projects on AWS.

Starting with an introduction to ML on AWS, you will learn the fundamentals of ML and explore important AWS services for artificial intelligence (AI). You will then see how to store and process data for ML using several AWS services, such as S3 and EMR.

You will also learn how to prepare data for ML and discover different techniques for data manipulation and transformation for different types of variables. The book covers the handling of missing data and outliers and takes you through various ML tasks, such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with their specific ML algorithms, that you need to know in order to pass the exam. Finally, you will explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them.

By the end of the book, you will have gained knowledge of all the key fields of ML and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML. This book is not only intended to support you in the AWS Machine Learning Specialty certification exam but also to make your ML professional journey a lot easier.