Edge MLOps: An Automation Framework for AIoT Applications
Emmanuel Raj, David Buffoni, Magnus Westerlund, Kimmo Ahola
Abstract
Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT operations and decision making. Edge computing is emerging to enable AIoT applications. In this paper, we develop an Edge MLOps framework for automating Machine Learning at the edge, enabling continuous model training, deployment, delivery and monitoring. To achieve this, we synergize cloud and edge environments. We experimentally validate our framework on a forecasting air quality situation. During validation, the framework showed stability and automatically retrained, integrated, and deployed models for specific environments when their performance deteriorated under a certain threshold.