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1
Intro
2
What is IoT
3
Project Objectives
4
Architecture
5
Architecture Diagram
6
UI Features
7
Challenges Learnings
8
Distributed Deployment
9
Deployment Steps
10
Service Weight Strategy
11
Go Application Code
12
Observability
13
Testing Issues
14
Project Shift
15
Project Update
16
Questions
Description:
Explore an AI/ML data pipeline processing infrastructure for healthcare use cases using open source EdgeX Foundries microservices. Learn about the architecture, design, and integration of EdgeX features for edge devices in this conference talk. Discover how to automatically detect, manage, and process images from OEM equipment using containerized microservices and various communication APIs. Gain insights into challenges faced, including distributed deployment scenarios, timing-dependent issues, and integration test idempotency. Understand the implementation of wait strategies for dependent services and the project's shift towards observability. Dive into the UI features, Go application code, and deployment steps for this adaptable solution that leverages open source technology at the edge.

AI/ML Data Pipeline Processing with Go Microservices at the Edge - Open Source Solution

Linux Foundation
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