8bit.tr Journal
Dataset Versioning and Rollbacks: Provenance for LLM Training
How to version datasets, track lineage, and roll back safely when training data changes.
Why Versioning Is Essential
Data changes silently can cause model regressions.
Versioning provides accountability and reproducibility.
Lineage Tracking
Track sources, filters, and transformations.
This allows you to trace issues back to root causes.
Rollback Strategies
Store snapshots of datasets used for each model version.
Make rollbacks easy and automated.
Operational Controls
Require approvals for dataset updates in production.
Log all changes for auditability.
Evaluation Integration
Run regressions when data changes, not just when models change.
This catches subtle drift early.
Storage and Metadata
Store dataset hashes alongside schema versions.
Record sample counts and label distributions per version.
Track source licensing and usage constraints in metadata.
Use immutable storage for released dataset snapshots.
Log preprocessing parameters to enable exact reproduction.
Maintain a dataset manifest for quick comparisons.
Capture data quality scores to guide approvals.
Document data removal events to satisfy compliance needs.
Operational Rollbacks
Define rollback triggers based on quality or safety regressions.
Automate rollback workflows where possible.
Keep rollback checklists so teams act consistently.
Verify downstream model compatibility before reverting.
Maintain a rollback window for each release.
Communicate rollback outcomes to stakeholders promptly.
Archive rollback events for audit and learning.
Test rollback paths regularly to ensure readiness.
Set rollback thresholds per domain to avoid blanket reversions.
Capture rollback duration to track operational efficiency.
Include rollback validation tests before reopening traffic.
Document dependencies that may block rollbacks.
Use canary rollbacks to reduce risk during reversions.
Store rollback decisions with supporting evidence.
Maintain rollback owners so accountability is clear.
Keep a recovery SLA for high-impact regressions.
Log rollback outcomes with model and data versions.
Automate notifications to affected teams on rollback.
Maintain rollback drills to validate readiness ahead of releases.
Keep a rollback decision log so future audits can trace actions.
FAQ: Dataset Versioning
Is it overkill for small teams? No, it prevents costly surprises.
What is the fastest win? Store dataset hashes and metadata.
What is the biggest risk? Untracked data updates causing regressions.
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