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Real-World Edge Computing

Real-World Edge Computing

By : Robert High, Sanjeev Gupta
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Real-World Edge Computing

Real-World Edge Computing

By: Robert High, Sanjeev Gupta

Overview of this book

Edge computing holds vast potential to revolutionize industries, yet its implementation poses unique challenges. Written by industry veterans Rob High and Sanjeev Gupta, this comprehensive guide bridges the gap between theory and practice. Distilling expertise from their combined decades of experience in edge computing and hybrid cloud mesh solutions, this book equips software developers and DevOps teams with the knowledge and skills needed to deploy edge solutions at scale in production environments. It also explores foundational standards and introduces key factors that may impede the scaling of edge solutions. While edge computing draws from the successes of cloud computing, crucial distinctions separate the two. High and Gupta elucidate these distinctions, helping you grasp the nuanced dynamics of edge-computing ecosystems. With a focus on leveraging Open Horizon to overcome pitfalls and optimize performance, this book will help you confidently navigate the intricacies of constructing and deploying resilient edge solutions in real-world production settings. By the end of this book, you’ll have acquired a deep understanding of essential success factors for building and deploying robust edge solutions in real-world production settings, leveraging Open Horizon for scalable edge deployments.
Table of Contents (24 chapters)
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1
Part 1: Managing the Edge
7
Part 2: Working on the Edge
13
Part 3: Advancing the Edge System
17
Part 4: Edge Management in Practice

Use of MMS

Traditionally, using an ML-based inferencing service API running in the Cloud is an accepted approach. However, for reasons such as the need for low latency, data privacy, lower cost, and autonomous operation, the application may want to perform the task of ML model-based inferencing at the Edge.

That will require that the ML model be delivered to the Edge node where needed.

As an alternative solution, some applications may choose to wrap the ML model along with the application code itself within the same container and deploy that composite image to the Edge node. Given the life cycle of frequently updating the ML model, which is usually different from the application code, if you use the composite approach, every time the model is updated, you will need to transfer the entire composite application image to the Edge. This technique also has the disadvantage that your service will suffer downtime for a period while the old container is stopped and the updated container...

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