Machine learning models naturally degrade over time due to changing environments and dynamics.
Traditional ML monitoring methods focus on data drift and realized model performance, which can be limited.
A new ML monitoring workflow emphasizes estimating model performance in real-time and using drift detection for root cause analysis, reducing false alerts.