Model Cataloging | Teradata Package for R - Model Cataloging - Teradata Package for R

Teradata® Package for R User Guide

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for R
Release Number
17.20
Published
March 2024
Language
English (United States)
Last Update
2024-04-09
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Product Category
Teradata Vantage
tdplyr Model Cataloging feature is a deprecated feature in this release and is not guaranteed to work. It will be removed in future release versions.

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 Analytics Database via SQL, Teradata Package for Python (teradataml) or Teradata Package for R (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 Analytics Database (td_decision_forest_predict_sqle). Similarly, an ML Engine or Analytics Database 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 Analytics Database 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 the Teradata Package for R - tdplyr page on https://downloads.teradata.com/.

Some examples in the following sections use ML Engine analytic functions. These functions require that your system has the Vantage Machine Learning Engine, which is a separate machine learning legacy engine that is not part of the current standard Vantage offer. If your Vantage system does not have the required ML Engine, an error or no-op behavior will occur when these functions are invoked.