Integrating Scrum and MLOps: Adapting Agile Methodologies to the Machine Learning Lifecycle - Michał Opalski/ai-agile.org
Abstract The growing adoption of machine learning (ML) systems in production environments has exposed significant limitations of traditional Agile methodologies, particularly Scrum, when applied to data-driven and non-deterministic development processes. While MLOps has emerged as a set of engineering practices addressing deployment, monitoring, and reproducibility of ML models, it largely lacks an explicit integration with project management and Agile governance frameworks. This paper addresses this gap by analyzing the fundamental misalignments between Scrum and the machine learning lifecycle and proposing an integrated Scrum–MLOps framework. The proposed model reinterprets Scrum artifacts, roles, and metrics to accommodate experimental workflows, data-centric development, and continuous model evolution. We introduce novel sprint-level metrics tailored for ML projects and discuss empirical validation strategies through case studies. The results suggest that aligning Scrum with ...
