MLnq
MLnq is a term used to describe a family of approaches for connecting machine learning workflows with a queryable representation of models, datasets, experiments, and results. The aim is to improve reproducibility, discoverability, and interoperability across different ML platforms and tooling.
Overview: MLnq defines a core data model that treats entities such as Model, Dataset, Experiment, Run, and
Architecture and components: The typical stack includes a data store for MLnq entities, a schema registry, and
Usage and adoption: MLnq is used in research and industry prototypes to foster model governance, reproducibility,
See also: metadata management, data provenance, model registry, experiment tracking, MLflow, W3C PROV.