Hi Anandh,

"We have data analystics created those models in Python as the external model". I'm not understanding this exactly. But there are a couple of ways to incorporate models built outside of Pega.

1. Data level integration - i.e. you score the models then just use the results.
2. Translating the models into scorecards (or other constructs) in Pega. Not all models can easily be translated but for simple cases this may be feasible.
3. Importing through PMML. This is a model interchange format supported by Pega and by a large group of other vendors. Most ML tools support exporting to PMML. If this is possible, it's the preferred way.

Not sure what you mean by adaptive models you created in Python. Can you elaborate?

Adaptive models in Pega cannot be imported into. They are self-learning. What you could do is import the external model (one of the methods above) and have an Adaptive model shadow that. So initially you use the externally created model, but the Adaptive model will learn as it accumulates evidence. You should monitor the performance of the Adaptive model, and at some point you may decide to cut over.

PMML models in 7.3.1: https://docs-previous.pega.com/supported-pmml-model-types-pega-731

Mixing PMML, Adaptive and other types of models: https://mesh.pega.com/docs/DOC-18836