Comet's recent online panel featuring AI experts from Google, Stanford, and Hugging Face explored strategies for monitoring and retraining machine learning models in production. The discussion highlighted the importance of understanding model performance, tracking relevant metrics, and determining when retraining is necessary to maintain model accuracy and relevance. Piero Molino shared insights from a project at Uber, explaining that balancing speed and accuracy in customer support models had a significant impact, with retraining required approximately monthly to adjust for data distribution changes. Ambarish Jash emphasized the importance of continuous retraining pipelines to handle varying content freshness across different applications, such as restaurant versus YouTube recommendations, which demand different retraining frequencies. The panel underscored the need for dynamic approaches to model monitoring and retraining, accounting for both short-term performance and long-term model aging.