Hyper-parameter tuning and selection of winner model on the go
How can we design a flow, where hyper-parameter tunning happens in a single model(lets say random forest) and also training happens across different algorithms. And then, winner algorithm with its required hyper-parameter is selected and that is used in the prediction service?
