How would you implement model monitoring and a feedback loop in an AI system?

To implement model monitoring, I would set up a system to track key performance metrics, such as accuracy, latency, and user engagement. This can be achieved by using monitoring tools like Prometheus and Grafana for real-time dashboards. Additionally, I'd establish a feedback loop by collecting user interactions and model predictions, storing them in a database for analysis. Regularly retraining the model by incorporating this feedback data ensures the model remains relevant and adapts to changing user needs. This iterative approach helps in maintaining model performance over time and addresses issues like data drift.