https://aito.ai/blog/introducing-a-new-database-category-the...
The big difference of the predictive queries / databases compared to trained models is that predictive databases are prepared / optimized to allow making arbitrary predictions without pretrained model. So you can basically as to predict any X based on any A, B and C and expect a more or less immediate answer.
The benefit of not having pre-trained models relates to workflows and architecture, as described in the article. The disadvantage of having such instant generic prediction capability is that it's technical hard to implement, as described in the 'Quality' chapter.
Maybe I'm just a cynical data scientist, but this is how we get people using and interpreting models that they don't necessarily understand the complexities of. If some data violates an underlying assumption or has some complexities around representation and meaning then there's nothing really stopping someone getting a model that appears to fit correctly but gives answers that are meaningless or just wrong.
In traditional ML you need to a) define model to do prediction A -> B b) train the model, which may take minutes c) then do the predictions form A -> B, which takes (1, 10, 100) microseconds
With predictive queries, you: a) Ask prediction for any X based on any A, B and C and expect answers in (1, 10, 100) milliseconds
You basically trade throughput and latency to get higher productivity, faster iteration and simplified overall sysstem