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Check out ludwig, a driverless deep learning solution recently open-sourced by Uber. It uses tensorflow and is based on python.
So... with the right python/tensorflow installation in place, (tricky but a one-time affair) you could probably build an analytic app to create a deep neural net by, literally, having the user pick a dataset, check the target, check/uncheck predictors, apply some range limits if necessary... and off it goes: user builds a deep learning model without knowing anything at all about deep learning.
You could probably build a shap explainer into the same app and provide that as output along with the trained model; still without the user knowing anything other than that they can get pretty good predictions along with answers to "why that prediction".
In particular they have an entire example (requiring enterprise version to run, so no longer open source) of Automated Machine Learning that looks not unlike like Ludwig, except it is ready-to-go as point-and-click from a browser, including ample room for user input; heck Ludwig could even be tied into the back-end as one of the models considered. Very robust. Would love to see something like that in Alteryx. Seems doable.