The challenge of scaling machine learning (ML) at Uber is complex, involving not just the allocation of resources but also the management of a vast array of offline experiments across diverse markets and product segments. Unlike online A/B testing, these offline experiments aim to enhance the accuracy or performance of ML models without real-time deployment. At Uber, the logistical intricacies of conducting numerous experiments in various global markets necessitate a sophisticated experiment management system, prompting the integration of Comet, a tool that facilitates tracking and analyzing ML experiments. Comet supports Uber by organizing deep learning experiments and offering customizable features that allow different teams to add metrics specific to their projects, thereby improving the efficiency of ML infrastructure development. This collaboration is essential for Uber AI, which handles a wide range of state-of-the-art models, and highlights the importance of adaptability and extensibility in tools used to build ML infrastructure.