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

Technical requirements

To complete the hands-on exercises in this chapter, you will need to have completed the steps in Chapter 2, Foundations of Edge Workloads such that your edge device has been set up with the IoT Greengrass Core software running and the greengrass-cli component installed.

You will want to clone the chapter's resources from the book's GitHub repository, for ease of use, if you haven't already done so. There is a step included in the Connecting your first device – sensing at the edge section that enables you to clone the repository at https://github.com/PacktPublishing/Intelligent-Workloads-at-the-Edge/tree/main/chapter3. You can perform this step now if you would like to browse the resources in advance:

git clone https://github.com/PacktPublishing/Intelligent-Workloads-at-the-Edge- 

As a reminder, the hands-on steps for this book were authored with a Raspberry Pi and Sense HAT expansion board in mind. For those of you using other...