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

The technical requirements for this chapter are the same as those described in the Hands-on prerequisites section in Chapter 1, Introduction to the Data-Driven Edge with Machine Learning. Please refer to the full requirements mentioned in that chapter. As a reminder, you will need the following:

  • A Linux-based system to deploy the IoT Greengrass software. A Raspberry Pi 3B, or later, is recommended. The installation instructions are similar to other Linux-based systems. Please refer to the following GitHub repository for further guidance when the hands-on steps differ for systems other than a Raspberry Pi.
  • A system to install and use the AWS Command-Line Interface (CLI), enabling access to the AWS Management Console website (typically, your PC/laptop).

You can access this chapter's technical resources from the GitHub repository, under the chapter2 folder, at https://github.com/PacktPublishing/Intelligent-Workloads-at-the-Edge/tree/main...