Using machine learning in DSS is a process in two steps:
The models are designed, trained, explored and selected in the Lab
Once you are satisfied with your model, you Deploy it from the lab to the Flow, where it appears as a Saved model
A Saved model is deployed together with a Training recipe that allows you to retrain the saved models, with the same model settings, but possibly with new training data.
A Saved Model on the Flow can be used:
For real-time APIs, using the API node
By a Scoring recipe, in order to perform prediction (or clustering) on a non-labelled dataset