This article proposes a practical, tool-free method for versioning datasets in machine learning to ensure reproducibility. It argues that maintaining a consistent data contract between pipelines and training processes is key, rather than relying on specialized tools like DVC or MLflow initially. The approach involves disciplined automation and metadata tracking, such as lineage and transformation details, before adopting more complex solutions. AI
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IMPACT Provides a lightweight, reproducible data versioning strategy for ML practitioners, reducing reliance on complex tools.
RANK_REASON The article presents a novel, practical approach to a common problem in machine learning research and practice. [lever_c_demoted from research: ic=1 ai=1.0]
