Excellent primer on higher-level languages and their applicability/appropriateness for enterprise-level development. Of course, it doesn't get to the crux of the argument: when it's more appropriate to use Scala/Erlang/Haskell instead of "less academic" languages. Typical among the academic community is the belief that computational efficiency doesn't matter for machine learning tasks when you're implementing them.
Of course, in practice, implementing an algorithm that performs well in experiments will only be slightly polished before putting it into production. Even in spite of the relative low cost of EC2 hosting, developers trying to maximize their ROI might find that the equation balances in their favor by maximizing efficiency on each core and decreasing the amount of memory that can be used. I admittedly don't know a lot about scaling experimental code into enterprise-level code, but I do know that at some point, it must reduce to a cost-benefit equation, where the costs of EC2 hosting is compared against the costs of development/debugging time in languages like C++. This, of course, reduces to the specifics of your business needs.
Of course, in practice, implementing an algorithm that performs well in experiments will only be slightly polished before putting it into production. Even in spite of the relative low cost of EC2 hosting, developers trying to maximize their ROI might find that the equation balances in their favor by maximizing efficiency on each core and decreasing the amount of memory that can be used. I admittedly don't know a lot about scaling experimental code into enterprise-level code, but I do know that at some point, it must reduce to a cost-benefit equation, where the costs of EC2 hosting is compared against the costs of development/debugging time in languages like C++. This, of course, reduces to the specifics of your business needs.