- 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 Specify the BYOM model name. Description Specify the description of the BYOM model. Format Select the format of the BYOM model: PMML, ONNX, H2O, H2O_DAI, Python, R, SAS Different properties need to be set in the Import sheet based on the different format selected for this property. See steps 6 to 8, respectively.Link to BYOM (Optional) Link to the 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 Specify the database that contains the data required by the model. Table Select the table from the drop-down list of available tables inside the database. Model Select the specific model from the list of available models stored within the selected table. Tags Add tags to the model for searches and filtering. Model monitoring Enable Model monitoring Select the checkbox to enable. Model type Select the type of the model: Classification or Regression. Training statistics Select the dataset used to train the model from the drop-down list. You can validate the dataset by selecting Validate.
Prediction expression Free text to parse the output values from the model predictions. You can validate the expression by selecting Validate. Use this expression to monitor the model.
Model evaluation & monitoring Select the checkbox to enable feature and prediction drift monitoring. This is used to compute statistics after importing a model.
Select to enable model evaluation and performance monitoring. If enabled, you can monitor and evaluate the model performance; otherwise, you can directly approve and then deploy the model.
Select the Metrics Type: default or custom.
If custom is selected, you must upload the metrics; otherwise, metrics will be computed using the dataset you specified in the Training statistics drop-down list.
For PMML,ONNX, H2O, H2O_DAI Import sheet, set the following properties: Tab Property Description Details External Id Specify the model name. Tags Add tags to the model for searches and filtering. 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 the dataset used to train the model from the drop-down list. You can validate the dataset by selecting Validate.
Prediction expression Free text to parse the output values from the model predictions. You can validate the expression by selecting Validate. Use this expression to monitor the model.
Model evaluation & monitoring Select the checkbox to enable feature and prediction drift monitoring. This is used for computing statistics after importing model.
Select to enable model evaluation and performance monitoring. If enabled, you can monitor and evaluate the model performance; otherwise, you can directly approve and then deploy the model.
Select the Metrics Type: default or custom.
If custom is selected, you must upload the metrics; otherwise, metrics will be computed using the dataset you specified in the Training statistics drop-down list.
For Python and R Import sheet, set the following properties: Property Description External Id Specify the model name. Tags Add tags to the model for searches and filtering. Model monitoring Select the checkbox to enable model evaluation and performance monitoring. If enabled, you can monitor and evaluate the model performance; otherwise, you can directly approve and then deploy the model.
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.