Netflix has published details of its internal 'Model Lifecycle Graph', a graph-based architecture that maps relationships between ML assets — datasets, features, models, evaluations, workflows, and production services — as interconnected nodes. The system addresses the operational complexity that emerges when large organizations accumulate many ML pipelines and deployed models across teams. Key goals include improving discoverability, enabling lineage traversal for impact analysis, reducing duplicated work, and supporting a self-service model for engineers and data scientists. The approach parallels similar metadata-centric platforms like LinkedIn DataHub, OpenLineage, and Uber's Michelangelo, and reflects a broader industry trend toward treating ML lifecycle governance as a core architectural concern rather than an afterthought.
