Data Versioning and Its Impact on Machine Learning Models
DOI:
https://doi.org/10.55662/JST.2024.5101Downloads
Keywords:
Machine Learning Models, Data Versioning, ML pipelineAbstract
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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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.