One way is to obviously go all out Java - definitely makes things streamlined. But not all team members are familiar with Java. Especially not ones formally trained on data science - who tend to work with R/python etc. Atleast that has been my experience.

As someone slightly involved in the Deeplearning4J[1] project it always surprises me that more people don't consider this option.

[1]: https://github.com/eclipse/deeplearning4j

Because all of the large tools are in Python. Tensorflow, PyTorch, OpenCV, Numpy, Pandas, etc.

The OP mentions that they are currently working in R.

The better question, is why Java? I don’t think I’ve ever encountered any company or person to use Java for ML. Scala yes. Clojure, surprisingly, yes. Java, no. Not to say they don’t exist, but it’s not a good idea. The ecosystem isn’t there, and the language (I want to say sucks), isn’t there either.

Where did you see clojure being used in production for ML? I am curious, because I am a clojure dev

We have used clj-ml[0], which wraps a bunch of Weka stuff, as well as the XGBoost JVM bindings. I've used Bayadera[1] and Dragan's other tools for linear algebra, although not in production. I was always sad that Incanter didn't really go anywhere, but at this point I wouldn't be surprised if Clojure became a respectable platform for data science, especially given Clojurists Together's funding focus this quarter[2].

0: https://github.com/shark8me/clj-ml

1: https://github.com/uncomplicate/bayadera

2: https://www.clojuriststogether.org/news/q4-2019-funding-anno...