Book Image

Intelligent Workloads at the Edge

By : Indraneel Mitra, Ryan Burke
Book Image

Intelligent Workloads at the Edge

By: Indraneel Mitra, Ryan Burke

Overview of this book

The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs. This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance. By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.
Table of Contents (17 chapters)
1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

Chapter 2: Foundations of Edge Workloads

This chapter will explore the next level of detail regarding edge workloads and your first hands-on activity. You will learn how AWS IoT Greengrass meets the needs for designing and delivering modern edge ML solutions. You will learn how to prepare your edge device to work with AWS by deploying a tool that checks your device for compatible requirements. Additionally, you will learn how to install the IoT Greengrass core software and deploy your first IoT Greengrass core device. You will learn about the structure of components, examine the fundamental unit of software in IoT Greengrass, and write your first edge workload component.

By the end of this chapter, you should start to feel comfortable with the basics of IoT Greengrass and its local development life cycle.

In this chapter, we are going to cover the following main topics:

  • The anatomy of an edge ML solution
  • IoT Greengrass for the win
  • Checking compatibility with...