# Fueling the AI Factory: The Central Role of the Metadata Catalog

- Institution: CERN
- Speaker: Jean-Thomas Acquaviva
- Year: 2026
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3025481)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

The rise of industrial "AI Factories," exposes a critical bottleneck that transcends raw storage performance: managing the data itself. As AI/ML pipelines ingest and process vast, heterogeneous datasets, the complexity of data discovery, lineage, and governance becomes a primary inhibitor to scaling operations. Traditional storage systems fail to answer vital questions: "Where is the verified, compliant dataset for training?", "What is the exact data lineage of this deployed model?", and "How do we optimize data placement across a distributed infrastructure?" This presentation details the design and implementation of the intelligent metadata catalog at the heart of the European project DaFAB. We demonstrate that a metadata-driven approach is the key to unlocking efficient, reproducible, and sovereign AI. We will cover: (1) The use of semantic search and active metadata for federated data discovery; (2) Automated data lineage and versioning to ensure model reproducibility and compliance; and (3) How the catalog integrates with the storage layer to orchestrate data movement, respecting data gravity to optimize for performance and cost. By treating metadata as a primary asset, the DaFAB catalog transforms the storage infrastructure from a passive repository into an active, intelligent component of the modern AI factory. The metadata catalog underlying technology used in DaFab, is the Rucio open-source technology originated from CERN. We will conclude by drawing some perspective with industrial solutions.

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## Lectures

1. [Fueling the AI Factory: The Central Role of the Metadata Catalog](https://invisiblenetwork.ai/learn/cern/3025481/8c2166217d08), 15 min (CERN's player)