在边缘设备上部署AI模型，实现工业质检实时化
EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection
- 用Cumulocity IoT与thin-edge.io构建边缘AI运维框架
- 在树莓派4上量化后推理速度比浮点快数倍
- 适合需要实时质检的工厂自动化场景
本文提出EdgeMLOps框架，利用Cumulocity IoT与thin-edge.io在资源受限的边缘设备上部署和管理机器学习模型。针对边缘环境中的模型优化、部署与生命周期管理难题，通过视觉质量检测（VQI）案例验证其有效性：边缘设备处理资产图像，实现实时状态更新。进一步评估了静态与动态有符号int8量化在Raspberry Pi 4上的性能表现，相比FP32精度显著降低推理时间。结果表明，EdgeMLOps可实现工业级边缘AI的高效、可扩展部署。
原文摘要 · Abstract (English)
This paper introduces EdgeMLOps, a framework leveraging Cumulocity IoT and thin-edge.io for deploying and managing machine learning models on resource-constrained edge devices. We address the challenges of model optimization, deployment, and lifecycle management in edge environments. The framework's efficacy is demonstrated through a visual quality inspection (VQI) use case where images of assets are processed on edge devices, enabling real-time condition updates within an asset management system. Furthermore, we evaluate the performance benefits of different quantization methods, specifically static and dynamic signed-int8, on a Raspberry Pi 4, demonstrating significant inference time reductions compared to FP32 precision. Our results highlight the potential of EdgeMLOps to enable efficient and scalable AI deployments at the edge for industrial applications.
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