Atomic Design for MLOps: A Modular Approach to Scalable and Reusable ML Pipelines

The increasing complexity of machine learning pipelines presents significant challenges in terms of maintainability, scalability, and reusability. This paper explores the application of atomic design principles—widely used in software engineering—to MLOps pipelines, structuring ML workflows into modular and reusable components. By decomposing AI pipelines into hierarchical elements, such as atoms, modules, stages, templates, and pipelines, this approach aims to improve efficiency, reproducibility, and collaboration in machine learning development. We describe the conceptual framework and its implementation in time series applications, including forecasting, classification, and anomaly detection. Preliminary results demonstrate the feasibility of this methodology, highlighting its potential benefits for streamlining ML workflows and reducing development overhead.