How does K-Means integrate into a full ML pipeline architecture?

K-Means is typically used as a preprocessing or feature engineering stage within ML pipelines.

In production pipelines, K-Means is rarely the final model. Instead, it generates cluster labels or distance features used by downstream supervised models. It is integrated with scaling, feature selection, and model validation stages.