How can I fix data drift in my machine learning model?

How can I fix data drift in my machine learning model?

Data drift occurs when the statistical properties of your training data change over time. To fix it, you can retrain your model with new data, monitor model performance regularly, and implement techniques like drift detection.

What are some techniques for detecting data drift?

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

How often should I check for data drift?

It's best to monitor for data drift continuously or at regular intervals, depending on how quickly your data changes. Setting up automated monitoring can help.

Can I automate the retraining process?

Yes, you can automate retraining by setting up a pipeline that triggers retraining when data drift is detected or based on a schedule.