I had to get down to the graph to realize they're talking about SVM, not deep learning.
This could be pretty cool. Training a SVM has usually been "load ALL the data and go", and sequential implementations are almost non-existent. Even if this was 1x or 0.5x speed and didn't require the entire dataset at once it's a big win.
there's still a ton of usage for classical learning algorithms. I'd be a very happy camper if we could speed SVMs up by a magnitude
Indeed, for relatively "simple" models, SVM can get very, very close to deep learning accuracy for classification, with only a fraction of the computing time needed.
A product from an IBM consultant is about as related to a product from IBM Watson as is a product from Microsoft being related to a product from Apple.
Sure they might have some divisions that do better, but I have yet to see them.