What are the best practices for handling data drift in machine learning?

What are the best practices for handling data drift in machine learning?

To handle data drift effectively, you should regularly monitor your model's performance, retrain with new data, and use drift detection techniques.

What techniques can I use to detect data drift?

Common techniques include statistical tests like the Kolmogorov-Smirnov test, monitoring model performance metrics, and using drift detection algorithms like ADWIN.

How often should I retrain my model to combat data drift?

It depends on the application, but a good practice is to retrain your model whenever you detect significant drift or at regular intervals, such as monthly.

Can you give examples of tools for monitoring data drift?

Sure! Tools like Evidently AI, Alibi Detect, and TensorFlow Data Validation are great for monitoring data drift.