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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

By : Indraneel (Neel) Mitra, Ryan Burke
4.8 (17)
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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

4.8 (17)
By: Indraneel (Neel) 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)
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1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

Reviewing the solution

Before we perform a solution review, let's restate the problem, revisit the target solution, and reflect on what was built in this book. This will help us refresh our memory and contextualize the solution review using the Well-Architected Framework.

Reflecting upon the solution

Our fictional narrative had us working at Home Base Solutions as the IoT architect responsible for designing a new home appliance monitoring product. This product was a combination of a hub device that connects to consumers' home networks and interacts with paired appliance monitoring kits. These kits are attached to consumers' large appliances, such as furnaces or washing machines, and send telemetry data to the hub device. The hub device processes telemetry data, streams it to the cloud to train ML models, and hosts local inference workloads using new telemetry and the deployed models. The following diagram shows how these entities are related in consumers'...

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