Organizing a machine learning project for success requires a holistic approach where model builders and application teams understand each other's roles and the implications of their work. Misuse of scores can lead to flawed training data, particularly if feedback loops are not considered. Ensuring that all team members are aligned on the purpose and application of scores is crucial for maintaining the integrity of the project.
