I've seen Python (and R) used all the time for exploratory ML. Do all of your feature extraction, feature selection, parameter tweaking, and backtesting in Python, and then once you have a model that works reasonably well, port the feature extraction for only the features that actually work well over to a compiled language like Java or C++, train your models on lots of data, and do your actual classification in the compiled language.

Most ML is an iterative process, and the final model that's used in production is just the tip of the iceberg of the development work that went on. Python works as well for exploratory programming there as it does for any other domain.

In research, Python is extremely popular since all the number crunching is done with numpy or Theano, which use BLAS and CUDA.

I've seen Python used heavily in ML where much of the work is done on the GPU anyways. There are plenty of libraries for that, and Python's slow nature isn't as relevant (though it still becomes relevant when CPU processing is the bottleneck!).

I work with a team that is doing large scale ML and CV on millions of pages and videos. It's a production system tied to millions of dollars in revenue. Everything's done in Python.