Most of researchers these days prototype using Python & R. However, when you put ML systems into productions, accuracy is not the only metric. Teams care about scalability and the speed of their system 1<p>What do you use when you deploy ML in production ?. Which technology make it easier for you to build faster and more reliable infrastructure.<p>Most of answers to this question on [reddit](https://www.reddit.com/r/MachineLearning/comments/7zizn3/d_python_scala_rust_or_go_what_do_you_use_when/). I am curious to see more diverse answers and why teams tend to use specific technology for ML ?