Observability Bridges Unknowns in Machine Learning?

In machine learning, the way that you've just described it seems like there are more failure modes that are external factors or internal factors. Can you shed a little more light on what sort of failure modes do you see in the AI ML space? And how does observability bridge that unknowns that you might see in the space to be able to diagnose and debug problems? Yeah. It's really hard to really say your model is failing unless you're talking about some various metrics. But a lot of the time, it's more like your model is slowly drifting away from the expected behavior. That slow drift over time is fundamentally hard and painful for human to notice. So typically over one week,