[_ptgt]

>But after you setup the dataset definition and defined the schema, the rest can be based on neural search?

Sure, but hyperparameter tuning and architecture selection takes such an insignificant amount of any competent ML practitioner's time so as to be pretty much irrelevant.

At least for me, my time is mostly spent: 1. Understanding (or designing) the process that generated the data. 2. Organizing the training schema. 3. Understanding the customer's business problem so that an appropriate ML system can be designed. 4. Doing an initial design of the ML system based on that understanding and then iteratively designing new components for said system based on customer feedback. 5. Developing or researching how to measure model performance. 6. Searching for alternative data sources. 7. Answering customer and stakeholder questions about the ML system 8. Implementing the ML system in code.

None of these can be automated with current technology, and there's a reason for that: if it was possible to automate a task then our team already would have.