Machine Learning on the AWS Cloud: Add Intelligence to Applications with AWS SageMaker and AWS Rekognition

Machine Learning on the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem, and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. The book is organized into two sections.

Part One introduces readers to fundamental machine learning concepts. Readers will learn about the types of machine learning systems, how they are used, and challenges they may face with machine learning solutions. Readers will also learn techniques that allow them to preprocess data, basic feature engineering, visualizing data, and model building.

Part Two focuses on machine learning services provided by Amazon Web Services. Readers are introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. They will then learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. The section concludes with coverage on the use of common neural network frameworks with Amazon SageMaker and solving computer vision problems with Amazon Rekognition. Appropriate illustrations, source code examples, and sidebars augment the content of each chapter.

The book will appeal to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/Solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.