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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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Free Chapter
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

Publishing an ML model using MMS

Now that you have seen ML model-based object detection happening at the Edge device node, we will use MMS to publish an updated ML model. In this example, to illustrate the deployment of the new model, we have created another version of the same model where some of the objects are wrongly labeled – for example, TV with radio, and so on. We will use two different models with two different metadata model publish files.

Important note

The focus of this example application is not the ML model itself but the delivery of the ML model to the edge nodes asynchronously using MMS.

To receive the MMS-published ML model and to make the model available to the application inference pipeline, this application deploys a standalone mms service. In this application design, the mms and infer services share a common directory structure where the newly received ML model is stored and then later retrieved by the infer service to reinitialize the inference...

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