CUDA is nowhere near the de facto in the ML industry. Beyond some specific application domains like CV where DL makes sense, most ML still runs on commodity hardware, and I happen to believe this will be the case for a long time once the Gartner Cycle of Hype settles on the Deep Learning craze and everyone runs out of VC money.

Despite what cloud providers and NVIDIA want you to believe, no you most likely don't need a lot of compute power for your industry problem. Basic knowledge of statistics would help though.