AI Data Lifecycle Advanced

AI data has a lifecycle from creation through active use to archival and deletion. Without lifecycle management, storage costs grow unbounded as teams accumulate datasets, checkpoints, and model artifacts. This lesson covers policies and automation for managing each type of AI data through its complete lifecycle.

AI Data Types and Retention

Checkpoint Management

Checkpoints are the largest source of storage waste in AI infrastructure:

• Checkpoint frequency — Save every N steps, not every epoch; tune based on training stability
• Rolling checkpoints — Keep only the last K checkpoints; automatically delete older ones
• Best-model tracking — Save checkpoints that achieve best validation metric; delete others
• Async checkpoint saving — Write checkpoints in background threads to avoid stalling training

Dataset Versioning

Version your datasets alongside your model code for reproducibility:

• DVC (Data Version Control) — Git-like versioning for large files; stores data in remote storage with pointers in Git
• Delta Lake / Iceberg — Table format versioning for structured datasets with time-travel queries
• Object storage versioning — S3 versioning for immutable dataset snapshots

Automated Cleanup Policies

• TTL-based deletion — Automatically delete temporary data (dev datasets, failed job outputs) after a defined period
• Quota enforcement — Set per-team storage quotas; alert and enforce when exceeded
• Orphan detection — Find and clean up data not referenced by any active experiment, model, or pipeline