Mission Lane's recent live demo showcased how they efficiently manage thousands of features for over two million customers in production, with Mike Kuhlen detailing their use of Chalk to streamline complex feature dependencies across various processes like training, live decisioning, and batch evaluation. By defining features once and reusing them across multiple applications, such as real-time apps and monthly batch jobs, they significantly reduce coordination overhead that previously required collaboration across data science, data engineering, and ML engineering teams. Chalk enables their models to request features by name, automatically fetching the necessary data without altering decisioning system code, which simplifies handling dependencies at scale and ensures up-to-date data for batch scoring. This approach has transformed their process, allowing data scientists to self-serve with Python code that is universally applicable, thus eliminating the need for extensive coordination across teams and systems.