Model Cataloging | Teradata Package for Python - 17.00 - Model Cataloging - Teradata Package for Python

Teradata® Package for Python User Guide

Teradata Package for Python
Release Number
November 2021
English (United States)
Last Update

Model Cataloging allows users to save model-related information in a way that it can be reused by the supported functions of Machine Learning Engine or Advanced SQL Engine via SQL, Teradata Package for Python (teradataml) or Teradata Package for R (tdplyr).

For example, an ML Engine DecisionForest model saved by using SQL can be retrieved to use with teradataml for scoring with the DecisionForestPredict function from ML Engine or Advanced SQL Engine. Similarly, an ML Engine or Advanced SQL Engine model saved by using tdplyr can be described and retrieved by teradataml. See save_model() for more information on what information is saved.

teradataml offers APIs to use Model Cataloging, allowing users to: It also provides mechanisms to persist models with teradataml, which would have been garbage collected otherwise.
Currently, teradataml only supports model catalog operations on ML Engine or Advanced SQL Engine generated models.

To use the teradataml offering related to model cataloging, the Model Catalog must be set up on Vantage system.

The scripts along with the instructions to set up the Model Catalog can be downloaded from the Teradata Package for Python - teradataml page on