My particular prediction for Machine Learning and Data Science is its going to become less and less Python vs R vs Julia. and more like the situation we have fo

My particular prediction for Machine Learning and Data Science is its going to become less and less Python vs R vs Julia. and more like the situation we have for for web-servers. Where basically every language has a solid quality webserver. And like how now if your focus as a company (etc) is on webstuff you'll use Node or something with a excellent webserver and will hire accordingly, but if you've got a big team that already uses java to make the desktop application, then you are not going to switch to Node (etc) for your new web offering: you will use the also very good TomCat or Jetty. Similar if you focus on complex modelling you'll use Julia/Python for that and you will just use their webserver libraries to expose it. The other way round will also occur (and definately already has started but i expect it to be more and more the case.) You are a web-company wanting to do some ML on some data you won't even think of having a seperate Python/R/Julia program, you will just use the Node equivs (which I am sure today are good, but I don't know them). Similar for the desktop applications in Java or C# they will just use their own ML / Data Science libraries. And just like there is indeed a role for specialized web servers like Node, there will still be case where you do want to pull out the big guns and move over to Python/R/Julia. but those will become rarer and rarer. I guess you could say it is commoditization of ML/Data Science libraries.