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?
You can use the python APIs to 1) retrieve an existing analysis and ml task from a saved model that you've configured and deployed to the flow manually (https://doc.dataiku.com/dss/latest/python-api/ml.html#obtaining-a-handle-to-an-existing-ml-task), 2) edit the settings of this ml task object to enable certain algorithms + hyperparameter combinations, 3) perform the multi-model retraining/hyperparameter tuning, 4) retrieve the best performing model and redeploy it to the flow over your existing saved model.
