Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.
Delve into Data Version Control (DVC), a tool for managing and versioning ML data. Explore its role in the ML lifecycle, differentiate data versioning from code versioning, and examine DVC’s features and use cases. Learn about DVC setup, including cache management and remotes, and discover its applications in CI/CD, experiment tracking, and pipelines. Automate ML pipelines, emphasizing code modularization, and practice executing them efficiently. Conclude with model evaluation, exploring metric tracking in DVC for informed decision-making.
Delve into Data Version Control (DVC), a tool for managing and versioning ML data. Explore its role in the ML lifecycle, differentiate data versioning from code versioning, and examine DVC’s features and use cases. Learn about DVC setup, including cache management and remotes, and discover its applications in CI/CD, experiment tracking, and pipelines. Automate ML pipelines, emphasizing code modularization, and practice executing them efficiently. Conclude with model evaluation, exploring metric tracking in DVC for informed decision-making.