LLM Fine-Tuning Data Governance: Stabilizing Model Quality with Versioning, Deduplication, and Contamination Detection
An engineering deep dive into governing LLM fine-tuning datasets as production assets. Covers data version tracking, multi-level deduplication, train/validation contamination detection, quality gates, and model release rollback for trustworthy, reproducible, and auditable management.