There exists a tool called TPOT (Tree-based Pipeline Optimization Tool) [0] that aims to automate the knob-twiddling that tends to go with optimizing Machine Learning models. As these models often have a number of parameters to tune and tweak over large scales, such a tool can be useful to identify performant combinations of these parameters and save time in doing so.

However, many ML practitioners are wary of similar automated ML pipelines, especially as they focus on non-expert users. A huge part of "data science" is the "data" itself. It often has idiosyncrasies and quirks that must be identified and accounted for in any model that hopes to make useful predictions. There are many pitfalls that come from not understanding the base statistical/mathematical assumptions of these tools, and a simplified Automatic ML Suite runs the risk of providing misleading results when used as a one-size-fits-all solution. Even for expert users, such tools often make it difficult (either by mathematical need or software design) to interpret the reasons and causes for their results. "Black boxes" like this are definitely hard to sell up the chain.

[0] https://github.com/rhiever/tpot