The intersection of Edge Computing, the Internet of Things (IoT), and Machine Learning Operations (MLOps) presents a transformative opportunity to enhance computational efficiency, data processing, and decision-making in real-time applications. This research seeks to address the critical challenges at this intersection, such as latency, privacy, and scalability, by harnessing the power of MLOps to innovate within Edge Computing and IoT environments. The primary objective of this scholarship is to develop a framework that integrates MLOps methodologies into Edge Computing and IoT systems, aiming to improve the deployment, monitoring, and management of ML models at the edge of the network. This framework will be designed to support dynamic adaptation to changing data streams, ensure data privacy and security, and facilitate efficient resource utilization.

The outcome will not only contribute to the academic body of knowledge but also offer practical guidelines for industry practitioners looking to leverage the benefits of AI at the edge.