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When ML models need to be regularly updated in production, a host of challenges emerges. Paramount among ML reproducibility concerns are the following:
No one tool can do it all for you – organizations using a mix of Git, Makefiles, ad hoc scripts and reference files for reproducibility. The following overview explains how DVC enters this mix offering a cleaner solution, specifically targeting data science challenges: First Impressions of Data Science Version Control (DVC) (full tutorial)
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