[grp000]

A predictive model, whether created by ML (regression, SVM, NN, whatever) or something rules-based born out of data analysis is reliant on quality data, which can be expensive to get. There is also a catch 22 where most of the models that are easy to make aren't practically usable because they're not needed in the first place, like a model that tells you if it's a nice day outside; most people would probably take a look at the weather and decide for themselves. On the other hand, a model predicting optimal stocks to buy or self-driving car models are worth a massive amount, but are also really hard to make. Companies will obviously try to sell bad or cheaply made models and may be successful on a small or niche level, but I think most people will recognize the utility and efficacy of a model based on the difficulty of the task it accomplishes relative to their own ability in that task, regardless of buzzwords associated with it. However, a lot of powerful modeling libraries made by really smart people are open source, so maybe what I'm saying is moot apart from sourcing the data.