Platform-agnostic MLOps on Edge, Fog and Cloud Platforms in Industrial IoT

Abstract

The proliferation of edge computing systems drives the need for comprehensive frameworks that can seamlessly deploy machine learning models across edge, fog, and cloud layers. This work presents a platform-agnostic Machine Learning Operations (MLOps) framework tailored for industrial applications. A novel framework enables data scientists in an industrial setting to develop and deploy AI solutions across diverse deployment modes while providing a consistent experience. We evaluate our framework on real-world industrial data by collecting performance metrics and energy measurements on training and prediction runs of two ML workflows. Then, we compare edge, fog, and cloud deployments and highlight the advantages and limitations of each deployment mode. Our results emphasize the relevance of the introduced platform-agnostic MLOps frameworks in enabling flexible and efficient AI deployments.