What are the signs of data drift in machine learning models?

What are the signs of data drift in machine learning models?

Data drift can manifest in several ways, including changes in data distribution, model performance degradation, and unexpected predictions.

How can I detect these changes in my model?

You can monitor key metrics over time, use statistical tests to compare distributions, and set up alerts for performance drops.

What tools can help with monitoring data drift?

There are several tools available, such as Evidently AI, Alibi Detect, and TensorFlow Data Validation, which can assist in monitoring data drift.

Is there a way to mitigate data drift once it's detected?

Yes, you can retrain your model with new data, adjust your feature engineering, or implement continuous learning strategies.