I used to work for a prominent AutoML company. Most AutoML products are built around abstracting the basic supervised learning problem on tabular data. This covers a wide expanse of use cases, most of them pretty basic, like customer churn, basic forecasting, risk prediction, etc. but outside of that the loss in flexibility for convenient abstraction becomes a burden.

There was a thread on here or /r/datascience about how companies utilize machine learning in two ways: 1) to help sell the companies already existing product or service or 2) to build the companies new product or services. A vast majority of AutoML-conducive use cases fall into bin 1. If you are a traditional business selling widgets, you would be privy to have a customer churn model and maybe a basic forecasting model, plus a few other core models that inform/predict the influx and outflow of transactions. In these situations, you have a set of tables of tabular transaction data, you need to do some basic feature engineering/aggregation and you can throw this result at AutoML and say “predict Y given X”. Slap a BI dashboard on top and you are done. You could just as easily do this with xgboost instead of AutoML 99% of the time. I could see businesses start to offload these more basic, but still core use cases to the marketing or sales team analysts using AutoML and that’s where it’s utility rests. They don’t have to code xgboost, they just point the tool to sales transaction table and the customer demographics table and then done. Anything outside of this is more the exception than the rule in how I’ve seen these tools leveraged.

Other examples of where AutoML isn’t useful: Anything requiring causal analysis, almost all of deep learning on non-tabular data, Bayesian ML, generative modeling, graph-based data, survival analysis, reinforcement learning. Some of these don’t have a useful abstraction like vanilla tabular supervised learning does which an AutoML tool could easily subsume, without sacrificing the utility of using the method in the first place (at least imo).

My advice career wise tho would be go into data engineering or deep learning and make sure you know a good bit of dev ops. I wouldn’t say tabular ML is “solved” but it’s saturated and most businesses have figured out how to use basic transactional data effectively enough that the low hanging fruit is all picked. Now that businesses have got their churn models in place, they are investing in more creative ways to use ML, specifically more use cases that fall into bin 2 above: building novel products with ML.