EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection

TL;DR

EdgeMLOps presents a framework for deploying and managing machine learning models on resource-constrained edge devices using Cumulocity IoT and thin-edge.io, demonstrated through a visual quality inspection use case with performance evaluations.

Contribution

The paper introduces EdgeMLOps, a novel framework that simplifies edge ML deployment and management, integrating Cumulocity IoT and thin-edge.io, with performance analysis on quantization methods.

Findings

Significant inference time reduction with signed-int8 quantization on Raspberry Pi 4.

Successful deployment of visual quality inspection models at the edge.

Demonstrated scalability and efficiency of EdgeMLOps in industrial scenarios.

Abstract

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.