Article Source

• Detecting drift; how to evaluate and explore data drift in machine learning systems

How to evaluate and explore data drift in machine learning systems

• Track: PyData: Machine Learning & Stats

Abstract

When your ML model is in production, you might observe input data and prediction drift. In absence of ground truth, drift can serve as a proxy for the model performance. But how exactly to evaluate it? In this talk, I will give an overview of the possible approaches, and how to implement and visualize the results.

• Recorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.
• https://2022.pycon.de
• More details at the conference page: https://2022.pycon.de/program/ASW8CJ