How do you design clustering systems that avoid single points of failure in production ML platforms?

Avoiding single points of failure requires distributed coordination, replication of metadata, and redundant compute nodes.

Production clustering systems must remain available even if nodes or services fail. This is achieved by distributing computation across multiple workers and replicating model metadata in durable storage. Coordination services like ZooKeeper or etcd ensure leader election and failover. Stateless compute nodes allow horizontal scaling and replacement without system downtime.