Hardest part of implementing a high-functioning production machine learning stack for me isn't the idea-articulating, prototyping, iterating, then polished refactoring. It's knowing when to go from a quick-producing language like Python/MATLAB/Julia to something painfully-written but smooth like C++ or into a scalable Elastic MapReduce or Mahout process (the former of which, sure, is language-agnostic).
You can only spend so much time optimizing on memory/CPU-times with smart data chunkings or low-dimensional representations or approximation operations. EC2 time and space is relatively cheap, but Python on a single machine with the multiprocessing module can only speed up by a multiple of < [# of Cores]...