1. Version Control System Type: DVC (Data Version Control) is specifically designed for machine learning models and data, while SVN (Subversion) is a general-purpose centralized version control system.
2. Tracking Data Files: DVC tracks data files separately from code, allowing for efficient management of large datasets, while SVN treats data files as part of the repository, which can lead to repository bloating.
3. Handling Large Files: DVC handles large files efficiently by using data file pointers, enabling versioning without storing multiple copies of the same file, whereas SVN struggles with large files due to its centralized nature.
4. Workflow Optimization: DVC optimizes workflows by providing commands for data and model versioning, making it easier to reproduce experiments and share results, while SVN lacks specific tools for optimizing workflow in a data-centric environment.
5. Collaborative Features: DVC offers collaborative features such as data pipelines to streamline collaboration on large datasets, enabling teams to work more effectively together, whereas SVN primarily focuses on code collaboration and lacks specialized features for data collaboration.
6. Data Lineage and Reproducibility: DVC tracks the lineage of data and models, ensuring reproducibility of experiments, while SVN does not offer built-in features for tracking data lineage and ensuring reproducibility.

In Summary, DVC and SVN differ significantly in their focus on handling data, workflow optimization, collaboration features, and ensuring reproducibility in a machine learning-specific environment.