Version Control in Data Management: Optimizing Machine Learning Model Performance

Abstract

As machine learning (ML) models become increasingly integral to decision-making processes, managing their development, deployment, and ongoing optimization is crucial. Version control, a cornerstone in software engineering, is emerging as a vital component in the data management lifecycle to ensure reproducibility, collaboration, and performance optimization of ML models. This paper explores the role of version control in the context of data management, focusing on its application to optimize machine learning model performance. We discuss the challenges associated with model versioning, data drift, and the evolving nature of ML pipelines. Leveraging version control systems tailored for data and models, we present strategies to address these challenges, emphasizing the importance of traceability, reproducibility, and collaboration. We delve into the integration of version control with continuous integration/continuous deployment (CI/CD) pipelines, facilitating the seamless transition from development to deployment. Furthermore, the paper investigates the impact of version control on model interpretability, governance, and compliance, highlighting the potential benefits of maintaining a comprehensive version history. Case studies and practical examples demonstrate how version control enables the tracking of changes in model hyperparameters, feature engineering techniques, and dataset shifts, ultimately contributing to the iterative improvement of ML model performance.