Spark New Zealand, the country's largest telecommunications and digital services company, was faced with the challenge of understanding their customers' needs at a granular level to provide better services. The company aimed to expand the number of machine learning (ML) use cases across the organization to achieve this goal. They started their journey in machine learning by trying to predict churn and understand customer preferences. However, as the number of use cases and the size of the team expanded, they faced issues with model performance and monitoring. The dynamic nature of data and the need for continuous monitoring and troubleshooting of models posed significant challenges. Checking the performance of over 50 models every week was a tedious and time-consuming task. The company needed a solution that could help them monitor these changes more effectively and proactively approach the output of models.