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.
- Save information and related objects to the catalog for the model created using tdplyr analytic functions, using td_save_model();
- List the saved models, using td_list_models();
- Describe a saved model, using td_describe_model();
- Retrieve a saved model for reuse, using td_retrieve_model();
- Publish a saved model to set its access level and status, using td_publish_model();
- Delete a saved model, using td_delete_model().
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/.