What is concept drift and how to react to it?

Concept drift is a situation in machine learning where the statistical properties of the data on which the model is trained change over time. This leads to a deterioration in the model's quality because it does not account for new patterns.

Example: A model predicting product demand trained on last year's data, but customer behavior has changed due to new trends.

How to respond to concept drift:

• Monitoring model quality — regularly check metrics on new data.
• Updating the model — retrain the model on fresh data or use online learning methods.
• Using adaptive algorithms that can adjust to data changes.
• Detecting drift — apply statistical tests or specialized algorithms to identify changes in data distribution.

Thus, it is important to have a system that monitors and quickly reacts to data changes to maintain the relevance and accuracy of the model.