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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

Diving deeper into AWS services

This book focused on a specific use case as a fictitious narrative to selectively highlight features available from AWS that can be used to deliver intelligent workloads to the edge. There is so much more you can achieve with AWS IoT Greengrass, the other services in the AWS IoT suite, the ML suite of services, and the rest of AWS than what we could cover in a single book.

In this section, we will point out a few more features and services that may be of interest to you as an architect in this space, as well as offer some ideas on how to extend the solution you've built so far to further your proficiency.

AWS IoT Greengrass

The Greengrass features we used in the solution represent a subset of the flexibility that Greengrass solutions can offer. You learned how to build with components, deploy software to the edge, fetch ML resources, and make use of built-in features for routing messages throughout the edge and the cloud. The components...

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