AI teams segment data lifecycles to reduce costs by moving inactive datasets to cheaper storage tiers. They checkpoint training progress regularly and back up checkpoints to cloud storage to prevent loss from failures. Models get protected via object locks, automated backups, and geo-redundant storage for disaster resilience. Teams analyze egress fees upfront to avoid costly data transfer charges when switching cloud providers. They calculate replication overhead to balance storage costs with latency, staging data near GPUs for faster training.
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