Training data isn't always an issue. There are plenty of methods that don't require labels or use "weakly labelled" data.
Since most contemporary methods only make sense if lots of training data is available in the first place, many companies interested in trying ML do have plenty of manually labelled data available to them.
Their issue often is that they don't want to (or can't for regulatory reasons) send their data into the public cloud for processing. Any major speed-up is welcome in these scenarios.
Since most contemporary methods only make sense if lots of training data is available in the first place, many companies interested in trying ML do have plenty of manually labelled data available to them.
Their issue often is that they don't want to (or can't for regulatory reasons) send their data into the public cloud for processing. Any major speed-up is welcome in these scenarios.