ML Drift - How to Identify Issues Before They Become Problems
Over time, our AI predictions degrade. Full Stop.
Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy.
Attend this meetup to understand the key types of machine learning drift and how to catch them before they become problems.