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DVC, the leading tool for data versioning and machine learning (ML) workflow management, has released DVC 3.0, featuring updates to the entire DVC stack. These updates include model registry, cloud compute for experiments, and cloud versioning, making it easier to track experiments and develop ML models collaboratively. Additionally, DVC 3.0 introduces features such as Python logging library DVCLive, a DVC extension for VS Code, and Studio for collaboration. Hyperparameter optimization is also made simple with DVC 3.0, and users can manage their entire model lifecycle within their Git workflow.
