How do you make K-Means robust in production ML systems?

Robust K-Means requires preprocessing, outlier handling, initialization control, and monitoring drift.

Production systems require data validation, scaling pipelines, periodic retraining, and monitoring cluster stability. Outliers must be filtered, and drift detection must ensure clusters remain meaningful over time.