How would you design a large-scale distributed K-Means system?

You partition data across workers, compute partial centroids, and aggregate them iteratively.

Distributed K-Means splits data across nodes, each computing local centroid updates. A central coordinator aggregates results and redistributes updated centroids. This continues until convergence. Frameworks like Spark implement this efficiently.