The fastest way to make progress on these business questions is often to build hacky MVPs that look like they're doing something smart, but behind the scenes are powered by humans or dead-simple algorithms, and get them in front of customers ASAP.
I recently joined a seed-stage startup solving a business problem via audio analysis in the manner I described above. I'm not spending much time doing ML yet, but I'm banking on my belief that we're solving a valuable problem (customers want to buy our hacky MVP) and that ML can and will be needed to scale our solution. By deeply understanding the customer as a first step, I think the ML systems we build will be business critical and enduring. Time will tell
I recently joined a seed-stage startup solving a business problem via audio analysis in the manner I described above. I'm not spending much time doing ML yet, but I'm banking on my belief that we're solving a valuable problem (customers want to buy our hacky MVP) and that ML can and will be needed to scale our solution. By deeply understanding the customer as a first step, I think the ML systems we build will be business critical and enduring. Time will tell