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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

Using data to build machine learning (ML) models

In this section, you will read about techniques for efficient (re)training, inferencing, deployment, and customizing ML models. We will also discuss what has prevented high levels of demand from being met, and what is being done to resolve that.

Before we dive into the topic, it’s appropriate to briefly review Artificial Intelligence (AI) and what distinguishes it from ML and Deep Learning (DL). IBM describes AI as “leverage[ing] computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.” See “What is Artificial Intelligence (AI)?” in the Suggested pre-reading material section at the beginning of the chapter for a deeper explanation and some background history. ML is a branch of AI and a component of the field of data science that uses data and algorithms to imitate the way we believe human brains acquire knowledge. ML typically uses structured or labeled...

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