Data scientists often face challenges with version control, resulting in numerous iterations of Jupyter notebooks and confusion over which APIs to use when deploying models. Traditional tools like GitHub and GitLab partially address these issues but leave gaps, especially in tracking which models are in production and facilitating collaboration with business units. The difficulty in organizing and understanding APIs further complicates the workflow. To address these challenges, a new solution offers features such as model upload and versioning with comments, real-time visibility of model usage, and the ability to switch model versions in production easily. It also includes tagging systems for models and APIs to enhance organization and scalability, aiming to bridge the gap between data teams and other organizational units, thereby streamlining workflows and improving delivery of results.