Historical Backfill Pipeline

The Challenge

We migrated our experiment tracking stack to a new architecture, but we had hundreds of valuable historical runs locked in legacy systems (Weights & Biases) without the metadata required for our new Vector Search system or the artifacts needed for reproducibility.

The Solution

I built a Retroactive Continuity (‘Retcon’) Pipeline that behaved like a migration job for ML artifacts:

• Extraction: developed crawlers to systematically pull checkpoints, metrics, and configs from W&B histories.
• Enrichment: A batch processing engine that rehydrated models, ran them through our modern feature extraction pipeline, and generated missing metadata.
• Unification: Re-ingested the enriched artifacts into MLflow with full lineage, creating a single source of truth ready for downstream search and recommendations.

The Impact

Allowed us to launch the new Recommendation System with a fully populated database on Day 1, leveraging years of historical R&D data that would otherwise have been discarded.