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 9: Fleet Management at Scale

The Internet of things (IoT) had a humble beginning in 1999, in Procter & Gamble, when Kevin Ashton introduced the idea of integrating a radio-frequency identification (RFID) antenna into lipstick shelves to enable branch managers to better track cosmetic inventories for replenishments. Since then, this technology has been adopted across all industry segments in some form or another and has become ubiquitous in today's world.

Managing a set of RFID tags, sensors, and actuators inside a known physical boundary is a relatively easy task. However, managing millions (or billions or trillions) of these devices globally throughout their lifecycle is not. Especially when these devices are spread across different locations with various forms of connectivity and interfaces.

Therefore, in this chapter, you will learn about the best practices of onboarding, maintaining, and diagnosing a fleet of devices remotely through AWS native services...