Machine Learning Models

Every model is e machine learning model, and a i model is probablistic, which means it never gives you guaranteed results. It also is probably not being used on real world, not the way that the data is meant. And there's a miss match, right? There's a mis allignment with expectations. So if you are looking to do something, we always say, look at it as a pilot, because that's going to give you really good information about how this works. But in't got to take a look like houses doing, against real key performance indicato. How much time did we really save? How many errors did we truly reduce? How many whatever the measurement