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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

Summary

In this chapter, you were introduced to the DevOps and MLOps concepts that are required to bring operational efficiency and agility to IoT and ML workloads at the edge. You also learned how to deploy containerized applications from the cloud to the edge. This functionality allowed you to build an intelligent, distributed, and heterogeneous architecture on the Greengrass-enabled HBS hub. With this foundation, your organization can continue to innovate with different kinds of workloads, as well as deliver features and functionalities to the end consumers throughout the life cycle of the product. In the next chapter, you will learn about the best practices of scaling IoT operations as your customer base grows from thousands to millions of devices globally. Specifically, you will learn about the different techniques surrounding fleet provisioning and fleet management that are supported by AWS IoT Greengrass.

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