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1. Define KPIs and thresholds for model 

   1. Edit the provided Data-drift.dmn file to reflect your desired tolerance for data drift

   2. 

   3. Repeat for the provided Concept-drift.dmn file Performance-test.dmn

   4. Save the files locally to your machine.

2. Associate Monitor models to snapshot

   1. Navigate to the specific model snapshot

3. Using the Associated Models widget, create a data drift association

   1. Image RemovedImage Added

   2. Use the provided data and the DMN you made in step 2.

      1. Use the provided data and the DMN you made in step 2.

      2. 

4. Before leaving the Model snapshot screen, copy the ID from the URL bar, you’ll need this for later

   1. 

5. To test, run a monitoring job manually

   1. Make a REST call to MOC’s automation engine to trigger a data drift detection job on your model

      1. Obtain a valid auth token

      2. Make a call to the MLC API to initiate the monitor:

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This model uses a Spark MulticlassClassificationEvaluator to determine the accuracy of the predictions generated by the titanic model.

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