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 10: Reviewing the Solution with AWS Well-Architected Framework

You now have the skills required to create edge machine learning (ML) solutions. This chapter acts as both a summary of the key lessons that have been learned throughout this book and follows through on why they are best practices by reviewing the delivered solution. By reviewing the solution, we can see how the Home Base Solutions prototype hub design holds up and where there are further opportunities to improve it. You will learn what it is like to perform a deep analysis of the solution using the AWS Well-Architected Framework, a mechanism that was created for reviewing complex solutions. Finally, we'll leave you with suggested next steps for your journey as a practitioner of delivering intelligent workloads to the edge.

In this chapter, we're going to cover the following main topics:

  • Summarizing the key lessons
  • Describing the AWS Well-Architected Framework
  • Reviewing the solution...