Navigating Model Drift: Lessons from the Trenches
When I started working with Drift, I quickly realized there was a gap between theory and what actually happens in practice. This post is about understanding model drift and setting up automated retraining. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Model Drift As I delved into the world of machine learning operations (MLOps), I encountered a critical challenge that can make or break the performance of a model in production: model drift. It's a phenomenon where the underlying relationships between the input data and the predicted outputs change over time, causing the model's accuracy to degrade. My experience with model drift has been a journey of discovery, filled with mistakes, lessons learned, and a deeper understanding of how to navigate this complex issue. Concept Drift and Data Drift There are two primary types of model drift: concept d...