Comment by dweinus

I don't want to hate, what you built is really cool and should save time in a data scientist's workflow, but... we did this. It won't "automate most of the ML lifecycle." Back in ~2018 "autoML" was all the rage. It failed because creating boilerplate and training models are not the hard parts of ML. The hard parts are evaluating data quality, seeking out new data, designing features, making appropriate choices to prevent leakage, designing evaluation appropriate to the business problem, and knowing how this will all interact with the model design choices.