Model catalog

Machine learning algorithms which support the train mode produce trained models which are stored in the Model Catalog. Similarly, predict procedures can use such trained models to produce predictions. A model is generally a mathematical formula representing real-world or fictitious entities. Each algorithm requiring a trained model provides the formulation and means to compute this model.

The model catalog is a concept within the GDS library that allows storing and managing multiple trained models by name. This chapter explains the available model catalog operations.

Name Description


Prints information about models that are currently available in the catalog.


Checks if a named model is available in the catalog.


Drops a named model from the catalog.

Stores a names model from the catalog on disk.


Loads a named and stored model from disk.


Removes a named and stored model from disk.


Makes a model accessible to all users.

Training models is a responsibility of the corresponding algorithm and is provided by a procedure mode - train. Training, using, listing, and dropping named models are management operations bound to a Neo4j user. Models trained by a different Neo4j user are not accessible unless explicitly published.