ML Drift - How to Identify Issues Before They Become Problems // Amy Hodler // MLOps Meetup #89

MLOps Community Meetup #89! Last Wednesday we talked to Amy Hodler, Evangelist, Responsible AI of Fiddler. //Abstract 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. Catch this meetup to understand the key types of machine learning drift and how to catch them before they become problems. // Bio Amy helps organizations see how they can achieve more responsible AI by improving machine learning explainability, accuracy, and bias detection. As the AI evangelist for Fidder Labs, she educates data scientists on the use of continuous monitoring for modern MLOps. Amy is the co-author of O’Reilly