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

Connecting your first device – sensing at the edge

In this section, you will deploy a new component that delivers the first sensing capability of your edge solution. In the context of our HBS appliance monitoring kit and hub device, this first component will represent the sensor of an appliance monitoring kit. The sensor reports to the hub device the measured temperature and humidity of an attached heating, ventilation, and air conditioning (HVAC) appliance. Sensor data will be written to a local topic using the IPC feature of IoT Greengrass. A later section will deploy another component that consumes this sensor data.

If you are using a Raspberry Pi and a Sense HAT for your edge device, the temperature and humidity measurements will be taken from the Sense HAT board. For any other project configurations, you will use a software data producer component to simulate measurements of new data. Component definitions for both paths are available in the GitHub repository, in the...