[gidim]

A common error is target leaking. An AutoML system will likely consider this a "strong feature". This is where having someone that actually understands the business domain is critical.

There's no question that there's value in AutoML system yet most ML production systems I've worked on / seen were way more complex than feature vector -> model -> prediction. You likely have multiple models, pipelines, normalizations and plain old conditionals. Hard to automate all of this.

[streetcat1]

Right. I am aiming at the group of companies that have 0 data scientist and would like to avoid hiring one. I assume that their use cases is simple/common and can be automated.

Note that automation is not only building the model, but automating the full life cycle - pre processing, hp optimization , pipeline deployment and monitoring/retraining.