Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph
Netflix has transformed its machine learning infrastructure to support diverse business domains, overcoming fragmentation by implementing a Metadata Service and Model Lifecycle Graph, enabling cross-domain collaboration, discovery, and exploration of ML assets.
MAIN POINTS
- Netflix's machine learning evolved from personalization to multiple domains like studio, payments, and ads.
- Fragmented ML tools hindered cross-domain collaboration and model discovery.
- Metadata Service (MDS) and Model Lifecycle Graph connect ML entities for exploration.
- MDS enriches metadata, enabling lineage, impact analysis, and entity exploration.
TAKEAWAYS
- MDS enables real-time ingestion and enrichment of ML metadata for cross-domain collaboration.
- The Model Lifecycle Graph facilitates exploration of ML assets, enhancing discovery and reuse.
- Challenges include tool integration, metadata quality, and advanced relationship inference.
- The AIP Portal provides a unified interface for ML practitioners to explore and navigate ML assets.