Hey Luke! I'm a full stack / devops person so this is going to be high level but getting a couple of my ML researcher colleagues in for a deeper dive now. Shor

Hey Luke! I'm a full stack / devops person so this is going to be high level but getting a couple of my ML researcher colleagues in for a deeper dive now. Short version: 1) we can get to the same level of accuracy with around 10% of the data points. Getting (and managing) a big enough data set to train a supervised learning model is the biggest thing slowing down ML deployments. 2) the model contacts a human when it can't label a data point with a high degree of confidence. You'll never have people with a bunch of specialist knowledge being asked to perform mundane data labelling tasks