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

Samenvatting

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 de
velopment, 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 pipe
lines. 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 ap
plications. This framework empowers data scientists and engineers to collaborate seamlessly, fostering a culture of continuous 
improvement and innovation in the ML lifecycle.