Data Phoenix Events

Abstract

In this talk, we go over different machine learning monitoring techniques that can help us ensure our models continue to perform as expected after being deployed into production. We will discuss performance monitoring to track metrics like accuracy and latency over time, log analysis and telemetry data to debug errors, as well as explainability methods to identify and address predictions that deviate from our desired baseline.