How can I handle drift in my machine learning models?

How can I handle drift in my machine learning models?

Handling drift involves monitoring model performance and updating the model as needed. You can use techniques like retraining with new data or implementing adaptive algorithms.

What types of drift should I be aware of?

There are two main types of drift: covariate drift, where the input data distribution changes, and concept drift, where the relationship between input and output changes.

How often should I check for drift?

It's recommended to monitor your models continuously, but the frequency of checks can depend on the application and how quickly the data changes.

Can you suggest tools for monitoring drift?

Sure! Some popular tools include Alibi Detect, Evidently AI, and WhyLabs, which can help you monitor and visualize drift.