Data Version Control

DVC is an open-source version control system for machine learning projects. It helps teams manage datasets, models, and pipelines in a reproducible way, similar to how Git manages source code. The platform provides data versioning, experiment tracking, and pipeline orchestration, making it easier for data scientists, ML engineers, and enterprises to collaborate on large-scale ML workflows.

They offer dataset versioning, model management, pipeline automation, and integrations with Git, cloud storage (AWS S3, GCP, Azure), and ML frameworks like PyTorch and TensorFlow, alongside features for collaboration tools, secure infrastructure, and scalable storage. DVC also supports remote storage backends, experiment comparison dashboards, and CI/CD integration. These capabilities help organizations improve reproducibility, streamline ML lifecycle management, and scale data science projects.

And the best part? DVC provides a forever free open-source edition with core features, plus a 14‑day free trial of DVC Studio (cloud platform) for advanced collaboration and visualization. Paid enterprise plans unlock extended storage, managed hosting, and dedicated support.