Do custom inference models capabilities differ from vanilla

[IraWatt]

Hi, I have been looking at custom model deployments and wanted to know if their capabilities in MLOps differ from standard deployments using DataRobot's inbuilt models. Specifically can custom models use continuous AI features such as automatic retraining?

[IraWatt]

Thanks @dalilaB, Both MLDev and custom Inference models are trained models before being monitored by MLOps, I assume then what is passed to the model registry just contains model information (without the code used for training that model ect)?

I assume it could be done with a parameter of some kind allowing DR to pass training data to the model, if the model registry had that ability.

[dalilaB]

From my understanding, DataRobot will not be able to use continuous AI automatic retraining, because one assumes that these models are trained models monitored by MLOps.  For instance, the python models are pickled (serialized) before being put in out MLOps and monitored.  I cannot see how automatic retrain will work. However, you can change the model with another model.