Worked on the UWARG/computer-vision-python repository to enhance machine learning model deployment and asset governance for a computer vision pipeline. Focused on organizing and deploying pre-trained ML models, specifically versions 11s and 11n, by creating a dedicated models folder to streamline versioning and improve accessibility. Leveraged Python and model management best practices to upload a trained and tuned 11s model as a reference asset, establishing a repeatable pattern for future model integration. This approach supported scalable workflows by ensuring models are discoverable and readily available for development and testing, addressing asset management challenges in machine learning-driven computer vision projects.