Tag: LLM Fine-tuning

2 articles

• LLM Fine-tuning Dataset Version Governance in Production: Using Data Lineage, Evaluation Gates, and Rollback Strategies to Prevent SFT Degradation

  A practical guide to versioning LLM fine-tuning datasets in production, covering sample lineage, train-test splits, evaluation gates, experiment tracking, rollback strategies, and pre-release checklists to prevent SFT degradation from dirty data, biased samples, or incorrect labels.

  07/07/2026 LLM Fine-tuning / SFT / Dataset Governance / MLOps / Evals

• 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.

  07/03/2026 LLM Fine-Tuning / Data Governance / Data Versioning / MLOps / Production AI