Dealing with Machine Learning Model Drift

Hey all,

I have been working with a machine learning model in my project recently, and I have encountered a persistent issue called “Model Drift.” It’s been quite a challenge to manage, so I wanted to share my experience and seek some advice on handling this issue effectively.

There are several factors that contribute to model drift

  • Data Evolution
  • Concept Drift
  • Environmental Changes

When I Search About it, I came across to these resources/article Machine Learning Tutorial Codecrew Codewithchris Community forum and as per them I need to simplify my queries to reduce their complexity, or wait for the budget to reset before retrying the query​.

I’m currently exploring different approaches to mitigate model drift in my project. Who have dealt with similar challenges. How do you manage model drift in your machine learning projects?

Any tips or insights would be greatly appreciated… :slightly_smiling_face: