The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS.
Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them.
By the end of this book, you'll have gained knowledge of the key challenges in machine learning 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.
Understand all four domains covered in the exam, along with types of questions, exam duration, and scoring
Become well-versed with machine learning terminologies, methodologies, frameworks, and the different AWS services for machine learning
Get to grips with data preparation and using AWS services for batch and real-time data processing
Explore the built-in machine learning algorithms in AWS and build and deploy your own models
Evaluate machine learning models and tune hyperparameters
Deploy machine learning models with the AWS infrastructure