What are the common challenges in ML model monitoring?

What are the common challenges in ML model monitoring?

Some common challenges include data drift, model performance degradation, and lack of real-time monitoring.

How can I detect data drift in my models?

You can use statistical tests like the Kolmogorov-Smirnov test or monitor feature distributions over time.

What tools can help with model performance monitoring?

Tools like MLflow, Prometheus, and Grafana are popular for monitoring model performance.

Is it important to have real-time monitoring?

Yes, real-time monitoring helps in quickly identifying issues and maintaining model accuracy.

What should I do if my model's performance drops?

You should investigate the cause, retrain the model with updated data, or adjust the model parameters.