Successful machine learning teams excel at managing the data loop, continuously collecting and organizing data to improve model predictions. Clear ownership of each part of the process allows for easier adjustments and monitoring, making the entire ML workflow feel seamless. In contrast, teams lacking this structure struggle with updates and often remain stuck with outdated models. Great ML applications hinge on the effectiveness of their flywheel.
