This article discusses the limitations of current MLOps practices, which typically version code, containers, and models but neglect the data used in each execution. It proposes an "operating control" approach that emphasizes versioning the data state to ensure reproducibility and reliability in machine learning workflows. AI
IMPACT Highlights a gap in current MLOps tooling, suggesting a need for better data versioning to improve ML system reliability.
RANK_REASON The item is a blog post discussing a conceptual approach to MLOps, not a release or research paper.
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