Model Training Storage Lifecycle from Dataset to Archive

The model training storage lifecycle moves data through tiers based on value and access frequency — hot tier for active training data, warm tier for recent checkpoints, cold tier for archives — with retention and deletion policies that keep storage cost aligned with the data's diminishing value over time. For the storage architecture, see AI storage architecture requirements. For cost planning, see storage cost for AI workloads.

The Lifecycle Tiers

Hot tier (active): high-throughput storage for datasets currently feeding training, and the most recent checkpoints for fast recovery. Expensive per GB but GPUs need the throughput. Warm tier (recent): medium-throughput storage for checkpoints from completed training runs that may be needed for recovery or fine-tuning — accessible but cheaper than hot tier. Cold tier (archive): low-cost storage for completed training artifacts, old dataset versions, and checkpoints retained for compliance — accessed rarely or never. Deletion: data that has no remaining value or whose retention period has expired is deleted with evidence. Automated tiering policies move data through the lifecycle based on age, access frequency, and retention rules. For the governance, see storage governance checklist.

FAQ

What is the lifecycle of AI training storage?

Data moves from hot (active, high-throughput) to warm (recent checkpoints, accessible) to cold (archive, rare access) to deletion. Automated policies based on age and retention rules manage the transitions. See the tiers above.

Summary

Training storage lifecycle tiers data from hot to cold to deletion. For the full framework, see AI storage architecture.