Chapter 1: Introduction to the Data-Driven Edge with Machine Learning
The purpose of this book is to share prescriptive patterns for the end-to-end (E2E) development of solutions that run at the edge, the space in the computing topology nearest to where the analog interfaces the digital and vice versa. Specifically, the book focuses on those edge use cases where machine learning (ML) technologies bring the most value and teaches you how to develop these solutions with contemporary tools provided by Amazon Web Services (AWS).
In this chapter, you will learn about the foundations for cyber-physical outcomes and the challenges, personas, and tools common to delivering these outcomes. This chapter briefly introduces the smart home and industrial internet of things (IoT) settings and sets the scene that will steer the hands-on project built throughout the book. It will describe how ML is transforming our ability to accelerate decision-making beyond the cloud. You will learn about the scope of the E2E project that you will build using AWS services such as AWS IoT Greengrass and Amazon SageMaker. You will also learn what kinds of technical requirements are needed before moving on to the first hands-on chapter, Chapter 2, Foundations of Edge Workloads.
The following topics will be covered in this chapter:
- Living on the edge
- Bringing ML to the edge
- Tools to get the job done
- Demand for smart home and industrial IoT
- Setting the scene: A modern smart home solution
- Hands-on prerequisites
