Data Symphony: Harmonizing Version Control for Peak Machine Learning Performance

Abstract

In the landscape of machine learning (ML) development, version control plays a pivotal role in ensuring reproducibility, collaboration, and the maintenance of model performance. However, the unique challenges posed by ML projects, such as large datasets, complex model architectures, and hyperparameter tuning, necessitate specialized version control systems. "Data Symphony" is introduced as a comprehensive framework designed to harmonize version control practices specifically tailored for ML workflows. Leveraging key principles from software engineering and ML best practices, Data Symphony orchestrates a seamless integration of version control tools, facilitating efficient collaboration and optimization of ML models. This paper presents the architecture and components of Data Symphony, highlighting its capabilities in enhancing ML development processes and improving model performance.