During August 2025, Samuel Yuen focused on improving machine learning asset governance and deployment readiness for the UWARG/computer-vision-python repository. He developed and implemented a structured approach for organizing pre-trained ML models, specifically versions 11s and 11n, by deploying them into a dedicated models folder. This work, carried out using Python and leveraging best practices in machine learning model deployment, enhanced versioning, discoverability, and accessibility of assets for the computer vision pipeline. By establishing a repeatable asset management pattern, Samuel enabled more scalable and maintainable workflows, though the scope was limited to a single feature without direct bug fixes during this period.