What are the best practices for managing drift in machine learning models?

What are the best practices for managing drift in machine learning models?

Managing drift involves monitoring model performance, retraining models regularly, and using techniques like ensemble methods.

How often should I retrain my models to handle drift?

It depends on the data and application, but a common practice is to retrain models every few weeks or months.

What tools can help me monitor drift?

Tools like MLflow, Seldon, and DataRobot can help monitor model performance and detect drift effectively.

Can you explain what data drift and concept drift are?

Data drift refers to changes in the input data distribution, while concept drift refers to changes in the relationship between input and output.