6.3 Using Machine Learning Models for Scoring and Prediction

The predictive model is an algorithm that you apply to streaming data to predict outcomes. In a pipeline, you use a predictive model in a scoring stage to do probability scoring.

In GoldenGate Stream Analytics, a predictive model is an ONNX file, that you upload and store in the system:

  • GGSA supports ONNX models with single-dimensional outputs of size 1. The output should be of int, float, double or Boolean datatype.

6.3.1 Importing a Predictive Model

To import a predictive model:
  1. On the Catalog page, click Create New Item, and select Predictive Model from the drop-down list.
  2. On the Type Properties screen, enter the following details and click Next :
    • Name
    • Description
    • Tags
    • Predictive Model Type
  1. On the Predictive Model Details, enter the following details and click Save:
    1. For Predictive Model URL, upload your ONNX file.
    2. In the Model Version field, enter the version of this artifact. For example, 1.0.
    3. (Optional) In the Version Description, enter a meaningful description for your model.
    4. In the Algorithm field, accept the default. The algorithm is derived from the model you have uploaded.
    5. (Optional) In the Tool drop-down list, select the tool with which you created your model.

6.3.2 Adding a Scoring Stage

To add a scoring stage:
  1. Open the required pipeline in Pipeline Editor.
  2. Right-click the stage after which you want to add a scoring stage, click Add a Stage, and then select Scoring.
  3. Enter a meaningful name and suitable description for the scoring stage and click Save.
  4. In the stage editor, enter the following details:
    1. Model name: Select the predictive model that you want to use in the scoring stage
    2. Model Version: Select the version of the predictive model
    3. Mapping: Select the corresponding model fields that appropriately map to the stage fields
You can add multiple scoring stages based on your use case.