modeltype

Modeltype is a term used in software systems, particularly in machine learning pipelines, to classify the kind of model referenced by a configuration, API parameter, or metadata tag. It does not describe a specific trained artifact by itself but indicates the category of model that should be loaded, evaluated, or deployed.

In practice, modeltype is stored as a string value in a configuration file or metadata record. Typical categories include classification, regression, time-series forecasting, clustering, and anomaly detection. Some platforms extend the set with domain-specific categories such as image_segmentation, text_classification, or sequence_prediction. The exact set is implementation-dependent, and it is usually complemented by an algorithm field that names the particular estimator or architecture, such as random_forest or transformers.

Purpose and usage: modeltype guides routing, validation, and processing within a pipeline. It helps determine which loading logic to apply, which evaluation metrics to use, and which preprocessing steps are appropriate. For example, a system may apply classification-specific metrics when modeltype is classification and use regression metrics when it is regression.

Relation to other identifiers: modeltype is distinct from the actual trained model artifact (the model) and from identifiers such as model_id or model_name. It serves as metadata that describes the broad category of the model, while the model contains the learned parameters and the identifiers locate the artifact.

Considerations: use a controlled vocabulary to avoid synonyms, document the meaning of each type, and ensure consistent capitalization and spelling across configurations. When a type is missing or unknown, systems should define a sensible default or raise a clear error.

In summary, modeltype is an auxiliary descriptor used to categorize models for loading, validation, and deployment, complementing other metadata in a model management system.