To learn from machine learning that is running in production, monitoring its performance and gathering user feedback is important. Regularly reviewing and analyzing the model's predictions and accuracy can help identify improvement areas and inform algorithm updates. Additionally, collecting user feedback and insights can provide valuable information on the model's use and where it may need to be revised. It is also essential to develop a continuous learning and iteration system, where updates and improvements are regularly incorporated into the model to ensure it constantly evolves and improves. Finally, building a solid team of data scientists and engineers who can stay on top of these updates and collaborate to optimize the model can help ensure its ongoing success.