Stop Blaming the Algorithm: It’s Your Feature Store! - A guide to Monitoring your Feature Store

Feature stores can fail silently, breaking models downstream that depend on the feature data. Over and over we see the root cause of model issues in production can be traced back to the data itself, not the model. By applying data monitoring to the feature store, practitioners can automatically catch data issues like missing values, change in data format or unexpected values (change in data cardinality), and data drift upstream before the models are impacted. In this presentation, you’ll learn how to get to the root cause of issues before they impact model performance, and we'll share advice on what should be monitored and where across your feature store and model infrastructure. Claire Longo, Head of MLOps Solutions Engineering, will cover implementation of data quality monitors, data drift monitors, and more.