How do you design a clustering system with strong consistency guarantees in distributed ML pipelines?

Strong consistency is achieved using synchronized updates, deterministic aggregation, and single-writer centroid updates in distributed clustering.

In distributed clustering systems, multiple workers compute partial updates simultaneously. Ensuring strong consistency requires enforcing a single source of truth for centroid updates, typically through a parameter server or coordinator node. Updates are either synchronized (bulk synchronous processing) or serialized to prevent race conditions. Deterministic reduction ensures identical results across runs. However, this comes at a performance cost, so many systems trade strong consistency for eventual consistency in large-scale setups.