CREATE SOLUTIONS FOR VERSIONING AND MANAGING DATASETS USED IN AI AND ML.

It is also essential to correctly version and manage datasets to make them easily recognizable, traceable, and sharable throughout the various stages of AI & ML model development. Notably, there are many solutions to dataset versioning and management, with the best one touching on existing machine learning pipelines, highlighted by tools like DVC and MLflow, in this paper. To achieve this, the study provides simulation reports on using these tools in the current dynamic data environments, including healthcare, finance, and e-commerce, requiring robust version control mechanisms to counter quickly evolving data. Potential issues such as scale, data accuracy, and compatibility with present system adoptions are discerned with suggested solutions such as cloud-based management, checks and balances on data integrity, and ease of integration. The use of visuals shows how data lineage visualization helps in understanding the data flow for better implementation of measures and how different versioning tools compare in performance. The conclusions drawn from the study pertain to the fact that the implementation of structured data versioning strategies contributes to the enhancement of model quality and efficiency in addition to enhancing interaction between data scientists and engineers. This research finds that proper methods of developing and applying data versioning and data management practices are critical for effectively implementing AI and ML models in complex ecosystems that make decisions based on the most contemporary data. Future work will investigate the applicability of these tools as the number of data points to process increases, as well as the variability of those data points.