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Edge Computing Patterns for Solution Architects

Edge Computing Patterns for Solution Architects

By : Ashok Iyengar, Joseph Pearson
5 (8)
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Edge Computing Patterns for Solution Architects

Edge Computing Patterns for Solution Architects

5 (8)
By: Ashok Iyengar, Joseph Pearson

Overview of this book

Enriched with insights from a hyperscaler’s perspective, Edge Computing Patterns for Solution Architects will prepare you for seamless collaboration with communication service providers (CSPs) and device manufacturers and help you in making the pivotal choice between cloud-out and edge-in approaches. This book presents industry-specific use cases that shape tailored edge solutions, addressing non-functional requirements to unlock the potential of standard edge components. As you progress, you’ll navigate the archetypes of edge solution architecture from the basics to network edge and end-to-end configurations. You’ll also discover the weight of data and the power of automation for scale and immerse yourself in the edge mantra of low latency and high bandwidth, absorbing invaluable do's and don'ts from real-world experiences. Recommended practices, honed through practical insights, have also been added to guide you in mastering the dynamic realm of edge computing. By the end of this book, you'll have built a comprehensive understanding of edge concepts and terminology and be ready to traverse the evolving edge computing landscape.
Table of Contents (17 chapters)
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1
Part 1:Overview of Edge Computing as a Problem Space
4
Part 2: Solution Architecture Archetypes in Context
8
Part 3: Related Considerations and Concluding Thoughts

AI and edge computing

This is yet another type of convergence, that of AI and edge computing. Certain applications, such as autonomous vehicles on the road, healthcare monitoring, and industrial robots in an assembly line, require immediate responses because they do real-time analysis and are faced with making quick decisions. This is where deploying AI algorithms at the edge comes in because it brings intelligent decision-making to the edge and reduces the need to transfer data to central servers.

We talked about deploying AI models to the edge, but the training and retraining of those models are done on the enterprise edge or the regional edge and typically not done at the far edge. Even the deployment and management of these AI models across a large number of edge devices has its own challenges of scale and consistency. Not all devices are created equal, and neither are the AI models. Solution architects must be cognizant of the form factor of the edge devices, the constraints...

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