Abstract: According to some embodiments, a ML framework for an enterprise may include an analytics data store containing electronic records that include record characteristics to be processed by a data as a service layer to create data virtualization. A ML feature pipeline may perform feature engineering based on the data virtualization to create a ML model, and a consumer layer may process operational system information utilizing a business rule engine. In addition, a real-time inference platform may expose an API endpoint associated with deployment of the ML model using the data received from the consumer layer. A CI/CD platform may then automatically provide feedback information to the ML feature pipeline regarding performance of the deployed ML model.