DevOps in the Age of Machine Learning: Bridging the Gap Between Development and Data Science

Abstract

The increasing integration of Machine Learning models into software systems necessitates specialized DevOps pipelines, termed MLOps, to effectively manage their unique operational challenges. This paper presents best practices for integrating ML models into DevOps workflows, addressing key aspects such as data versioning, model testing, and continuous delivery of ML-driven applications. Traditional DevOps practices must be adapted to accommodate the iterative nature of ML model development, the complexities of data management, and the performance monitoring requirements of production ML systems. Our proposed framework provides a structured approach to building and deploying robust, scalable, and maintainable ML pipelines. We explore techniques for data version control, ensuring reproducibility and facilitating experimentation. Furthermore, we emphasize the importance of rigorous model testing, including offline evaluation, A/B testing, and continuous monitoring for performance degradation and concept drift. By adopting the outlined best practices, organizations can effectively bridge the gap between ML development and operations, accelerating the delivery and enhancing the reliability of MLpowered applications. This framework empowers data scientists and engineers to collaborate seamlessly, fostering a culture of continuous improvement and innovation in the ML lifecycle.