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Rejecting shadow models with MLOps

Updated on August 4, 2022

A shadow model runs in your production environment alongside an active model. The shadow model receives production data and generates outcomes, but does not impact your business decisions. The system tracks the outcomes to help you evaluate how the shadow model performs in production. If the model is not suitable for your needs, you can reject the model, and then replace it with a different one.

Rejecting shadow models is part of the Machine Learning Operations (MLOps) of Pega Platform.

Pega Customer Decision Hub
Before you begin: Review the performance of the shadow model. For more information, see Monitoring predictions.
  1. In the navigation pane of Prediction Studio, click Predictions.
  2. From the list of predictions, open a prediction that contains a shadow model, and then click the Models tab.
  3. Display the shadow model by expanding the twist arrow to the left of the current model.
  4. Ensure that the status of the model is SHADOW.
  5. To the right of the shadow model, click the More icon, and then click Reject model.
  6. In the Reject model dialog box, click Reject model to confirm.
Result: The shadow model is removed. You can replace the currently active model with a new candidate model. For more information, see Replacing models in predictions with MLOps.
    • Previous topic Promoting shadow models with MLOps
    • Next topic Updating active models in predictions through API with MLOps

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