How would you handle model drift in a deployed AI product?

Focus on identifying the signs of model drift and the potential impact it can have on your AI product's performance. Discuss strategies for monitoring model performance over time, including setting up alerts for performance degradation. Explain how you would implement retraining processes, such as using new data or feedback loops, to ensure the model remains accurate. Additionally, consider the importance of collaboration with data scientists and engineers in maintaining the model and how you would prioritize resources for ongoing model evaluation and improvement.