@nicebyte The typical crowdsourced computation like SETI@Home, or the protein folding thing is basically: get a small chunk of data, then crunch heavily on it for a long time (hours, days), then send the results.

Machine learning is the opposite, the main bottleneck is raw data throughput.

It's also wrong to assume that training itself is the primary problem. It isn't. Dataset preparation is. You spend the majority of your time (like 90+%) combing through the data.