I'm working on AutoML solution, that is available in the cloud (https://mljar.com). What is more, the core of my AutoML is open source (https://github.com/mljar/mljar-supervised) - both are easy to use. The cloud version has user interface so you need just to upload data and do few clicks. You don't need to have programming knowledge. For python package, you need to know how to manipulate data in python (basic numpy and pandas).

I've made many tests of my AutoML solution and I observe that Neural Networks doesn't work the best on tabular dataset (maybe not training long enough, but I dont have 2500CPU hours). I really prefer gradient boosting methods (xgboost, catboost, lightgbm) on tabular data. They are much faster than NN and require less preprocessing (no feature scaling).