EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection
This work addresses operational challenges for industrial applications using edge AI, though it is incremental as it builds on existing IoT platforms and quantization techniques.
The paper tackles the problem of deploying and managing machine learning models on resource-constrained edge devices by introducing the EdgeMLOps framework, demonstrating through a visual quality inspection use case that it enables real-time condition updates and achieves significant inference time reductions with quantization methods on a Raspberry Pi 4.
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.