Software Engineering for Machine Learning Systems (ML-SE)

Abstract

Machine Learning (ML) systems are increasingly integrated into criticaldomains such as healthcare, finance, transportation, and public services.However, traditional software engineering principles are often insufficient tomanage the complexity, uncertainty, and data-dependency inherent in MLbased systems. Software Engineering for Machine Learning Systems (ML-SE)has emerged as an interdisciplinary approach that integrates classicalengineering practices with data-centric development processes. This paperreviews the foundations, lifecycle models, challenges, tools, quality attributes,and best practices associated with ML-SE. It discusses how ML systems differ from conventional software, highlights the need for reproducibility, data versioning, monitoring, and continuous retraining, and examines frameworks supporting MLOps. The study also addresses ethical, reliability, scalability, and maintainability concerns. Finally, the paper outlines research directions to strengthen ML-SE methodologies. KEYWORDS: Machine Learning Systems, Software Engineering, MLOps,Model Lifecycle, Data Versioning, Continuous Integration, AI Deployment