[krallistic]

> The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has to be very high for it be productizable. You can do handwritten digit recognition with 90% accuracy?

It's way more nuanced than this. Of course, you need a decent "accuracy" (not necessarily the metric), but in many business cases, you don't need high accuracy. But you need a solid process: you can catch errors later, you can cross references etc, you need to failsafe, you need to have post-mortem error handling, etc...

I shipped stuff (classical ML) that was nothing more than "a biased coin flip," but that still generates value ($) due to the process around it.