Data version control tools are essential for managing machine learning projects by ensuring reproducibility, traceability, and proper lineage of ML models. The blog highlights seven tools: Neptune, Pachyderm, DVC, Git LFS, Dolt, lakeFS, and Delta Lake, each offering unique features to enhance workflow efficiency and collaboration. These tools facilitate the systematic handling of data by allowing users to track, version, and compare datasets and models, often integrating seamlessly with existing infrastructure. Choosing the right tool depends on factors like data modality support, ease of use, compatibility with existing systems, and team adoption. The blog emphasizes the importance of data versioning for building scalable and reliable ML pipelines and provides insights into how these tools can be integrated into an MLOps stack to optimize processes and improve team collaboration.