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 4: Extending the Cloud to the Edge

In the material leading up to this chapter, all of the development steps were performed on your device locally. Local development is useful for learning the tools and rapid prototyping but isn't representative of how you would typically operate a production device. In this chapter, you will treat your hub device as if it were actually deployed in the field and learn how to remotely interact with it using the cloud as a deployment engine.

Instead of authoring components on the device, you will learn how to use Amazon Web Services (AWS) IoT Greengrass to synchronize cloud resources, such as code, files, and machine learning (ML) models, to the edge and update devices via deployments. The tools, patterns, and skills you will learn in this chapter are important to your goals of extending the cloud to the edge and practicing how to manage an edge ML solution. You will connect a new client device to your hub device and learn how to bridge...