Implementing the air bubble detection ML model reduces the learning curve for scientists new to HPLC by demystifying one of the major error modes. Researchers will no longer need months or years of experience analyzing various pressure traces alongside experimental outcomes to pinpoint failures. Instead, this model represents a step toward making complex experimentation accessible across disciplines and skill levels. For the more experienced users who typically review key experimental parameters (such as column age, standard and blank traces in a neat window, etc) as part of their workflow during troubleshooting, the ML model predictions used for displaying the average bubble likelihood for an entire batch run-as well as the minimum and maximum values-providing a rapid quality assessment.