The proliferation of Internet of Things (IoT) devices and the increasing reliance on edge computing paradigms have ushered in a new era of distributed computing, where processing occurs closer to the data source rather than in centralized data centers. This shift promises to reduce latency, minimize bandwidth usage, and ensure data privacy and sovereignty. However, it also introduces significant challenges in managing, deploying, and maintaining machine learning (ML) models across a vast, heterogeneous, and geographically dispersed infrastructure. This doctoral research aims to address these challenges by advancing the integration of Machine Learning Operations (MLOps) practices within edge computing and IoT environments, facilitating the seamless deployment, monitoring, and management of ML models at the edge.