GPUs are quite good at doing arithmetic in parallel. A large part of machine learning is doing arithmetic on large data sets. It makes sense to do these operations in parallel. For example, implementing k-nearest neighbors on a GPU is almost 2 orders of magnitude faster than on a CPU[0].

GPUs just work very well when you have a a lot of data and you are able to run the operations on the data set in parallel. Machine learning seems to fit this model quite well which is why you see many GPUs used in this field. Other things that take advantage of parallelism would be graphics and crypto-currency mining.

[0] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.159...

The definition of an 'ML task' tends to change.

If you want to offer PaaS with FPGAs or ASICs, by all means. I'm sure there'd be some interest :)

ML might be a bit of a moving target though.

For some definition of "we", we are not.

Can you expand on that?

Why not?