Excited to see more people building in this space. From what we've seen with customers it's critical to be able to compare what you're seeing in production to what you trained on (rather than historical period). That's almost the textbook definition of drift. Do you have a sense on how to approach that?

At Comet.com (disclaimer: i'm the CEO/Co-founder) we provide experiment tracking and artifacts management so we have the training distributions for comparison. I'm always curious how it looks like for a monitoring only solution