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 Metric as first-class objects, with attributes like version, provenance, hyperparameters, training data lineage, evaluation results, and access controls. A dedicated query language, often referred to as the MLnq syntax, enables users to express complex questions such as “list models trained on dataset X with accuracy above 0.9” or “show all experiments that used a specific hyperparameter setting.”

Architecture and components: The typical stack includes a data store for MLnq entities, a schema registry, and an execution engine that translates MLnq queries into API calls to underlying ML platforms or to in-memory analytics. Adapters exist for common ML frameworks and experiment trackers, enabling integration with tools like model registries, artifact stores, and provenance systems. The emphasis is on schema-driven metadata that can be serialized in JSON-LD, RDF, or tabular forms.

Usage and adoption: MLnq is used in research and industry prototypes to foster model governance, reproducibility, and cross-system querying. Community-led efforts focus on standardizing entity definitions, query capabilities, and provenance semantics. Limitations include evolving schemas, varying levels of tooling maturity, and performance considerations for large data stores.

See also: metadata management, data provenance, model registry, experiment tracking, MLflow, W3C PROV.