ML metadata tracking

Every ML workflow generates a trail of facts that are worthless individually but priceless collectively: this run used that dataset version, these hyperparameters, ran for that long, produced this model with those metrics, and was consumed by that deployment. ML metadata tracking is the queryable store that records these facts and, crucially, the relationships between them, so the team can reconstruct, compare, and audit what happened across thousands of runs without trusting anyone's memory or a spreadsheet.

A metadata store models entities (datasets, runs, models, pipelines, deployments) and the lineage edges connecting them, capturing parameters, metrics, artifact pointers, code versions, and execution context. Because it is queryable, it answers operational questions directly: which runs used the dataset we just found a bug in, which production model has the best calibration, what changed between the run that worked and the one that regressed. It underpins experiment comparison, reproducibility, model registries, and governance, and it is the substrate continuous-training pipelines read and write as they execute.

The discipline that makes it valuable is automatic, structured capture at the moment work happens, not manual logging after the fact. Metadata recorded by hand is incomplete and untrustworthy; metadata emitted by the pipeline itself, with stable ids linking runs to their exact inputs and outputs, is what turns a pile of artifacts into a navigable, auditable history.