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.
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.
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].