Data Version Control (DVC) is a tool designed to track machine learning data and models, complementing Git's code tracking capabilities. This article provides a practical guide to using DVC, demonstrating its application with a wine-quality model. The tutorial covers setting up DVC with a cloud remote storage and integrating it into a continuous integration (CI) pipeline. AI
IMPACT Provides practical guidance on managing data and model versions in MLOps workflows.
RANK_REASON The article is a tutorial and explanation of a specific MLOps tool, DVC, rather than a new release or significant industry event.
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