Model Management refers to the systematic approach of overseeing and coordinating various aspects of machine learning models throughout their lifecycle. This includes tasks such as version control, performance monitoring, and deployment, ensuring that models remain effective and relevant over time. Effective Model Management helps organizations maintain high-quality predictions and insights by facilitating collaboration among data scientists, engineers, and stakeholders. It also involves documenting processes and decisions, which aids in compliance and reproducibility, ultimately leading to better decision-making and resource allocation.