This paper introduces an innovative MLOps platform specifically designed for edge AI, addressing critical challenges posed by limited computational resources constraints at the edge. The platform targets environments where inference is performed at constrained edge devices, while training is executed at resourceful on-premise or cloud servers. Utilizing a dual pipeline architecture, the platform continuously enhances model accuracy through server labeling and retraining processes, automating updates while ensuring stringent deployment quality standards. This platform provides a scalable blueprint for deploying robust edge AI solutions in dynamic environments, effectively leveraging powerful cloud resources to significantly enhance edge AI model performance. Our framework integrates state-of-the-art tools, including Apache Airflow, MLflow, and Apache TVM, to provide seamless and efficient lifecycle management of AI models optimized for edge environments. The platform has been validated using a satellite-based sea ice classification test bed. Targeting satellite AI applications, we present a comprehensive platform that integrates automated pipelines for data ingestion, ground-based image labeling, model retraining, version control, and optimized deployment. The test bed demonstrates the platform’s effectiveness in improving model accuracy through iterative labeling and retraining processes. Validation involved retraining a baseline model using an expanded dataset that included previously unrepresented ice classes. The updated model demonstrates a marked improvement in accuracy and detailed classification compared to the baseline, highlighting the platform’s capability to iteratively improve models based on new incoming data.