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Explore data versioning and machine learning experiment tracking using DVC (Data Version Control) in this 35-minute conference talk by Dmitry Petrov, Co-Founder & CEO of Iterative Inc., presented at the Toronto Machine Learning Series. Learn how DVC addresses the limitations of popular ML experimentation and metrics logging tools by providing reproducibility through integrated tracking of source code, training data, and metrics within Git repositories. Discover how this open-source tool efficiently manages data and models for hundreds of experiments using codification and metafiles, making it a feasible and effective approach for ML researchers and engineers.
