To effectively monitor machine learning models in real-time, you should establish clear performance metrics and thresholds that indicate when a model is underperforming. You should also set up automated alerting systems that notify relevant stakeholders when these thresholds are crossed. Additionally, you can use techniques such as A/B testing and anomaly detection to identify and resolve issues with your models as they arise.