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

Designing data patterns on the edge

As data flows securely from different sensors/actuators on the edge to the gateway or cloud over different protocols or channels, it is necessary for it to be safely stored, processed, and cataloged for further consumption. Therefore, any IoT data architecture needs to take into consideration the data models (as explained earlier), data storage, data flow patterns, and anti-patterns, which will be covered in this section. Let's start with data storage.

Data storage

Big data solutions on the cloud are designed to reliably store terabytes, petabytes, or exabytes of data and can scale across multiple geographic locations globally to provide high availability and redundancy for businesses to meet their Recovery Time Objective (RTO) and Recovery Point Objective (RPO). However, edge solutions, such as our very own connected HBS hub solution, are resource-constrained in terms of compute, storage, and network. Therefore, we need to design the...