I think value is still generated even if it doesn't solve 100% of the pipeline. Let's say you've got 2 steps: Prep the data, build the algo + infrastructure. L

I think value is still generated even if it doesn't solve 100% of the pipeline. Let's say you've got 2 steps: Prep the data, build the algo + infrastructure. Let's say for the sake of argument that they're about equal in terms of difficulty. If you can drastically reduce the amount of time the second takes, that will still be a valuable speedup. And I'll bet there's tools that could be included in these types of solutions that can help with data prep also (it doesn't have to be one or the other) Databases for instance -- there's lots of options, some proprietary, some open source -- but you'd rarely consider building your own unless as an intellectual exercise. At what point does this become true for machine learning? Saying it doesn't do 100% of my job for me doesn't mean that it won't work as a product category. Of course, this is coming from someone with a vested interest in the category, so take that with the requisite bias...