How can I prevent model drift in my machine learning models?

How can I prevent model drift in my machine learning models?

To prevent model drift, you can regularly monitor model performance, retrain with new data, and use techniques like ensemble learning.

What are some signs that my model is experiencing drift?

Signs of model drift include a decrease in accuracy, changes in input data distribution, and unexpected predictions.

How often should I retrain my models to avoid drift?

It depends on the application, but a good practice is to retrain models regularly, such as every month or after significant data changes.

Are there tools that can help monitor model performance?

Yes, tools like MLflow, TensorBoard, and Prometheus can help you monitor model performance and detect drift.