User's Guide: Build a Model

Build a Model

In the build a model phase, you can choose to run a selection of pre-curated AI algorithms on the datasets defined in the previous phases, to build AI models. The list of supported algorithms and their modes as well as the data types they supported for each mode are provided below.

NOTE
This stage is applicable only for tabular datasets.

For more details about the AI algorithms please refer to Confidential AI-Algorithms.
In the “Build a Model” form:

  1. Select the BUILD A MODEL tab and click BUILD MODEL to build a training model for the dataset created in the previous phase. CAI_BuildModel.pngFigure 1: Build a model
    In the “Build a Model” form:
  2. Enter the Training flow name, that is, the name of the model, for example: Patient_Workflow1.
  3. In the Training Dataset field, select the training dataset on which you want to run the AI algorithm and build a trained model.
  4. In the Algorithm field, select the algorithm that you want to run on the dataset to build the training model. The options available for selection are Logistic Regression, Decision Trees, Support Vector Machine, k-Nearest Neighbors, and scikit-learn Prediction.
  5. Select ML variables that you created in the Data Preparation phase.
  6. In the Model name field, enter the name of the output dataset. This is the output model that will be used in the data inference phase.
  7. Click BUILD MODEL to run the selected algorithm on the training data and build the model for inference. CAI__BuildModel.pngFigure 2: Build a model
  8. After the training model is built, you will see the model created under the Training flows. To run the training model, click the RUN button below the model.
    CAI_RunTrainingModel.pngFigure 3: Run training model
  9. You will notice the Running indication at the bottom of the workflow. At any point, if there is a need to stop the execution, click STOP. This will re-enable the RUN button. CAI_BuildRunning.pngFigure 4: Model training success
  10. If the model was executed successfully, you would see the status of the execution under the Execution Log. Click the Execution log link to view the log details. CAI_BuildSuccess.pngFigure 5: Model training success
  11. Click the download report icon to download the execution log report. CAI_ExecutionLog.pngFigure 6: Execution log
  12. After the execution is completed successfully, the model is now trained and ready for inference where it will be passed through a machine learning model for output data prediction.

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