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Updating active models in predictions with MLOps

Updated on May 17, 2024

As a data scientist, you can use Machine Learning Operations (MLOps) to approve changes to models that are used in predictions for deployment to the production environment. You can change models independently or by responding to a Prediction Studio notification that a prediction does not generate enough lift.

To improve the performance of a prediction, you can replace a low-performing model with a high-accuracy external model that you upload to a Pega repository or directly to Prediction Studio. As a result, you start a standard approval and validation process to deploy the model update to production. Before you approve any changes, you can compare the candidate model with the existing model based on data science metrics, such as score distribution or lift. For more information, see Active and candidate model comparison charts.

Managing model updates

In your Business Operations Environment (BOE), you can start and manage the model update process from Prediction Studio or remotely by using the Prediction Studio API. For more information about using the API endpoints, see Updating active models in predictions through API with MLOps.

Model deployment

In Pega Customer Decision Hub environments, changes to models that you approve in Prediction Studio are deployed to production through Pega 1:1 Operations Manager and the Business Change pipeline.

If you use decisioning models for other purposes, such as customer service or sales automation, components such as Pega 1:1 Operations Manager and Revision Manager are not present. A system architect merges the branch with the model update to the application ruleset that can be deployed to production.

For more information, see Understanding MLOps.

To replace a model in a prediction, and then deploy the model to production, perform the following procedures:

  1. MLOps prerequisites
  2. Replacing models in predictions with MLOps
  3. Evaluating candidate models with MLOps
  4. Promoting shadow models with MLOps
  5. Rejecting shadow models with MLOps

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