What is data drift and how does it affect AI models?

What is data drift and how does it affect AI models?

Data drift refers to the changes in the statistical properties of the input data over time. This can significantly impact the performance of AI models, as they may become less accurate if they are trained on outdated data.

Can you give me an example of data drift?

Sure! For instance, if a model is trained to predict customer behavior based on historical data, and then a new trend emerges (like a pandemic), the model may not perform well because the new data reflects different customer behaviors.

How can we detect data drift?

Data drift can be detected using statistical tests that compare the distributions of the training data and the incoming data. Techniques like the Kolmogorov-Smirnov test or monitoring model performance metrics can help identify drift.

What can we do to mitigate the effects of data drift?

To mitigate data drift, you can regularly retrain your models with new data, implement continuous monitoring, and use adaptive learning techniques that allow models to adjust to new patterns.