Looking toward the future, this work opens possibilities for automatic retrial of failed experiments in real-time and represents a crucial step toward fully closed-loop experimental automation. The framework could be expanded to detect other types of experimental anomalies as more data accumulates in the Cloud Lab. From a practical standpoint, this work helps prevent waste of valuable samples, reduces equipment downtime through predictive maintenance, and potentially lowers operational costs by preventing failed experiments.