Model Drift Awareness

Ravi emphasizes the importance of being aware of features prone to drift during sudden utilization shifts, like the pandemic. He stresses the need for proactive model planning and robust performance monitoring to mitigate risks. Kyle raises the question of responsibility between clinical leads and machine learning practitioners in identifying and addressing potential drift factors.