Defining a BYOM Model - Teradata Vantage

Select Projects in the Navigation bar.

Click a project in the List of Projects.

The List of Models for the selected project displays in the Work area.

Select Define BYOM Model.

In the Basic tab, set the properties:

Property	Description
Name	Specifies the BYOM model name.
Description	Specifies the description of the BYOM model.
Format	Lets you select the format of the BYOM model: PMML, ONNX, H2O, H2O_DAI, Python, R, SAS Based on the different format selected for this property, there are different properties need to be set in the Import sheet. See steps 6 to 8, respectively.
Link to BYOM	An optional property that allows linking to BYOM model workspace. It can be a GIT or DS workspace.

Select Save Model & Import Versions.

Set the properties in the Import model version window.

Based on the selection of Format, different properties are needed.

For SAS Import sheet, set the following properties
Tab	Property	Description
Details	Database
	Table	Select table from the dropdown list of available tables inside the database.
	Models inside table
Model Monitoring	Enable Model Monitoring	Select the checkbox to enable.
	Model Type	Select the type of the model: classification or regression.
	Training Statistics	Select training datasets from the dropdown list for validating statistics.
	Prediction Expression	Free text to input column expression for validating statistics.
	Model Evaluation and monitoring	Select the checkbox to enable feature and prediction drift monitoring. This is used for computing statistics after importing model.
	Model Evaluation and monitoring	Select to enable model evaluation and performance monitoring. If enabled, model will evaluate; otherwise, you can directly approve and then deploy model.
	Metrics Type	Select the Metrics Type: default or custom. If custom is selected, then user has to upload evaluation files.

For PMML,ONNX, H2O, H2O_DAI Import sheet, set the following properties
Tab	Property	Description
Details	External Id	Specifies the model name.
	Tags
	Upload model file	Upload control to upload only model file.
Model Monitoring	Enable Model Monitoring	Select the checkbox to enable.
	Model Type	Select the type of the model: classification or regression.
	Training Statistics	Select training datasets from the dropdown list for validating statistics.
	Prediction Expression	Free text to input column expression for validating statistics.
	Model Evaluation and monitoring	Select the checkbox to enable feature and prediction drift monitoring. This is used for computing statistics after importing model.
	Model Evaluation and monitoring	Select to enable model evaluation and performance monitoring. If enabled, model will evaluate; otherwise, you can directly approve and then deploy model.
	Metrics Type	Select the Metrics Type: default or custom. If custom is selected, then user has to upload evaluation files.

For Python and R Import sheet, set the following properties
Property	Description
External Id	Specifies the model name.
Tags
Model Monitoring	Select the checkbox to enable model evaluation and performance monitoring. If enabled, model will evaluate; otherwise, you can directly approve and then deploy model.
Model Monitoring	Select the checkbox to enable feature and prediction drift monitoring.
Upload model file	Upload control to upload evaluation files.

Deselecting Enable Model Evaluation does not allow the model version evaluation. For details, see Evaluating a Trained Model.

Select Import Version.

The imported model displays in the model versions list.