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 second device – actuating at the edge

The previously deployed component acts as a sensor to read values from a fictional appliance monitoring kit and publishes those values over IoT Greengrass IPC on a local topic. The next step is to create an actuator component that will respond to those published measurements and act upon them. Your actuator component will subscribe to the same local topic over IPC and render the sensor readings to the LED matrix of your Sense HAT board. For projects not using the Raspberry Pi with Sense HAT, the simulation actuator component will write measurements to a file as a proof of concept.

Installing the component

Similar to the previous installation, you will create a deployment that merges with the new component. Please refer to the earlier steps for the location of the source files and validation steps that the deployment concluded. For projects not using the Raspberry Pi with the Sense HAT module, you will deploy the com...