DLOps: Developing Deep Learning Aided Burmese Rice Price Forecasting

In nowadays technology areas, a pivotal concern has emerged. It’s was especially concerning architecting Deep Learning enabled Systems (DLS) and sustainability. Most of DLS were failed while release to production. That’s because of uncertainties in data variations, evolutionary requirements, and model inconsistencies. The one suitable solution is Deep Learning Operations (DLOps). It enhances reproducibility and sustainability in building DLS. It still has challenges and are related to environmental impact, technical maintenance, and economic concerns. The self-adaptation helps to address and overcome these challenges. In this paper proposed a sustainable modularized DLOps pipeline thorough the conceptual MAPE-K Framework to solve technical maintenance and economic concerns. The proposed system addresses the uncertainties in data handling and model training then try to proof out efficient utilization from economic concerns point of view. Further, the proposed system is implemented for a Burmese Rice Price Prediction use case to display DLOps pipeline sustainability from technical maintenance point of view.