[vonnik]

It's true that a lot of companies are at different stages of what we would call "the journey of digital transformation." Some of them do not know what's happening inside their plants. The problem before the problem -- the upstream chokepoint of ML -- is gathering, collecting and cleaning the right data. That's true across the industry and it's the main reason why ML is not moving faster, and why we don't see more huge ML-based startups. Scale, one of the unicorns, is chiefly devoted to data annotation... But there are a lot of smaller, well defined problems with machine scheduling and/or inventory management where they have exactly the data they need, and they are already feeding it to an optimizer. Deep RL can often outperform against those existing technologies.