Machine learning teams are increasingly transitioning from using platforms like GitHub to storing their models in specialized environments due to the unique challenges of deploying AI/ML at scale, which differ from traditional applications. This shift highlights the need for MLOps pipelines that stabilize and streamline model release processes while accommodating flexible experimentation. AWS Sagemaker, a managed service for building, training, and hosting machine learning models, offers abstraction and uniformity but may lack flexibility during deployment, especially for custom models. Additionally, the importance of reproducibility in machine learning is emphasized, with platforms like comet.ml providing tools similar to GitHub for tracking datasets, code changes, and experimentation history, thereby enhancing productivity, collaboration, and explainability. Comet.ml operates independently of infrastructure and machine frameworks, allowing developers to maintain their preferred training processes while ensuring a single source of truth for ongoing and completed machine learning projects.