How can we mitigate model drift in machine learning?

How can we mitigate model drift in machine learning?

Mitigating model drift involves techniques like retraining models, monitoring performance, and using adaptive algorithms.

What are some common causes of model drift?

Common causes include changes in data distribution, evolving user behavior, and external factors affecting the input data.

How often should we retrain our models to prevent drift?

It depends on the application, but regularly scheduled retraining, such as monthly or quarterly, is a good practice.

Can you recommend tools for monitoring model performance?

Sure! Tools like MLflow, Prometheus, and Grafana are great for monitoring model performance and detecting drift.