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.
Pega Customer Decision Hub
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.
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.
For more information, see Understanding MLOps.
To replace a model in a prediction, and then deploy the model to production, perform
the following procedures: