#experiments

DVC provides tools for managing machine learning projects by versioning data and models, building lightweight pipelines, and tracking experiments. It enables cloud data storage with version control via Git repositories, simplifying experiment comparison and sharing. The VS Code Extension facilitates experiment tracking and data management within the IDE, while DVC's diverse installation options ensure compatibility with various platforms.

DVCLive, a Python library, facilitates the logging of machine learning metrics and metadata in simple file formats. It integrates seamlessly with DVC, eliminating the requirement for extra services or servers to track experiments. Data is recorded as plain text files, allowing for versioning with Git or tracking in DVC storage, providing flexible data management. Metrics visualization is possible via DVC CLI or tools like the VS Code DVC Extension. Furthermore, its compatibility with platforms such as PyTorch Lightning and Scikit-learn underscores its adaptability across various machine learning environments.