[indeedmug]

There are some very cool features with MLJ. You can query models() and find all of the machine learning models that your data works with. This is great for easily plugging into various models to see what works.

The problem I ran into is that it can be very unclear when things don't plugin exactly as planned. You get a typing error that looks like C++ template errors. Or your data doesn't work quite right with a model and needs some transforming but the documentation doesn't spell how to do with your situation. There aren't nearly as many StackOverflow questions on using Julia as there are Python ones so you can't just look up a very specific question and get answers.

[Certhas]

Unfortunately this is a quite general issue with Julia at the moment. If things work it's magical, if they don't it's hard to see what subtle assumption of the packages used was violated. Julia and C++ Templates share a lot of properties, and some of the same pain points. It took C++ until C++20 to address this with Concepts. I hope it wont take quite as long for Julia.