Data Versioning and Its Impact on Machine Learning Models

Data Versioning and Its Impact on Machine Learning Models


  • Mohan Raja Pulicharla Department of Computer Sciences, Monad University, India




Machine Learning Models, Data Versioning, ML pipeline


Data versioning in machine learning is of paramount importance as it ensures the reproducibility, transparency, and reliability of ML models. In the dynamic landscape of ML research, where models heavily rely on diverse datasets, data versioning plays a crucial role in maintaining consistency throughout the ML pipeline. By tracking changes in datasets over time and aligning machine learning models with specific versions of data, researchers can reproduce experiments, verify results, and address challenges related to data quality, collaboration, and model training. Effective data versioning practices contribute to the robustness of ML workflows, fostering trust in model outcomes and supporting advancements in the field.


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DOI: 10.55662/JST.2024.5101
Published: 29-01-2024

How to Cite

Pulicharla, M. R. “Data Versioning and Its Impact on Machine Learning Models”. Journal of Science & Technology, vol. 5, no. 1, Jan. 2024, pp. 22-37, doi:10.55662/JST.2024.5101.
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