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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

Designing data patterns on the cloud

As data flows from the edge to the cloud securely over different channels (such as through speed or batch layers), it is a common practice to store the data in different staging areas or a centralized location based on the data velocity or data variety.These data sources act as a single source of truth and help to ensure the quality of the data for their respective bounded contexts. Therefore, in this section, we will discuss different data storage options, data flow patterns, and anti-patterns on the cloud. Let's begin with data storage.

Data storage

As we learned, in earlier chapters, since edge solutions are constrained in terms of computing resources, it's important to optimize the number of applications or the amount of data persisted locally based on the use case. On the other hand, the cloud doesn't have that constraint, as it comes with virtually unlimited resources with different compute and storage options. This makes...

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