Decoding MLOps: Bridging the Gap Between Data Science and Operations for Scalable AI Systems

Abstract

The increasing complexity of machine learning (ML) workflows and the demand for scalable AI systems have led to the rise of MLOps, a set of practices that bridge the gap between data science and operations (Bremer et al., 2020). This paper investigates the role of MLOps in enhancing the scalability, efficiency, and sustainability of AI systems across various industries. By automating model deployment, monitoring, and retraining, MLOps ensures continuous optimization and improved performance. This study demonstrates that organizations adopting MLOps experience significant improvements in operational efficiency, model accuracy, and time-to-market. Moreover, MLOps fosters better collaboration between data science and operations teams, resulting in higher stakeholder satisfaction. However, challenges remain, including the integration of diverse tools and the balance between flexibility and standardization. The findings suggest that MLOps plays a crucial role in scaling AI systems, offering valuable insights into the practical implications and future potential of its adoption.