Model Cataloging | Teradata R Package - 17.00 - Model Cataloging - Teradata R Package

Teradata® R Package User Guide

prodname
Teradata R Package
vrm_release
17.00
created_date
November 2020
category
User Guide
featnum
B700-4005-090K

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 Python Package (teradataml) or Teradata R Package (tdplyr).

For example, an ML Engine DecisionForest (td_decision_forest_mle) model saved by using SQL can be retrieved to use with tdplyr for scoring with the DecisionForestPredict function from ML Engine (td_decision_forest_predict_mle) or Advanced SQL Engine (td_decision_forest_predict_sqle). Similarly, an ML Engine or Advanced SQL Engine model saved by using teradataml can be described and retrieved by tdplyr.

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

To use the tdplyr 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 catalog can be downloaded from "Teradata R Package - tdplyr" page on https://downloads.teradata.com/.