Apache PredictionIO was designed for data scientists (I was a committer so am quite familiar with design decisions). It was built to allow new "engines" to be implemented for any arbitrary ML/AI algorithm. These came in "templates" of various flavors.

However it turned out that only the "Universal Recommender" template (that I wrote) had real demand and virtually no one was developing or using other "engines"

As designed it was hard to deploy and was not multi-tenant. I could go on about the shortcomings but suffice it to say that after I did the Universal Recommender and saw the PIO shortcomings, we (ActionML, my consulting company) decided to write a from-scratch ML server, called Harness, to solve the PIO issues and act as the host for continuing work on the Universal Recommender.

https://actionml.com/docs https://github.com/actionml/harness

So the best part of PIO (The Universal Recommender) lives on with a clean new design and modern architecture.