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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

Machine inferencing pipeline

Our object detection inferencing pipeline begins with capturing a frame from the incoming video stream. The video stream can be a prerecorded video file, an RTSP stream, or can come from an attached video camera. The frame is then passed to an ML object detector that applies the ML model on the frame to detect a set of objects that the ML model has been trained for. The detector output is a set of detected objects with their bounding boxes, usually a set of coordinates representing a rectangle. This detector output is used to annotate the frame with labels and draw a rectangle around the detected objects. In the interest of keeping the example simple, the annotated frame is then streamed as a Motion JPEG (MJPEG) file so that it can be easily viewed in a browser for verification of the detected objects. It’s possible to stream the detected objects with their bounding boxes to other services or to the Cloud. By locally processing the video stream,...

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