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 data over time, which can impact the performance of AI models.

Can you give me an example of data drift in a real-world scenario?

Sure! For instance, if a model is trained on customer data from a specific region, and then it is used in a different region with different demographics, the model's predictions may become less accurate.

How can we detect data drift?

Data drift can be detected using statistical tests, monitoring model performance over time, and comparing incoming data distributions with the training data.

What strategies can we use to mitigate the effects of data drift?

To mitigate data drift, you can retrain your models regularly, use adaptive learning techniques, and implement continuous monitoring systems.