For the air bubble contamination we focused on HPLC pressure traces. Since the pressure trace, by its nature, is a time series, we started our modeling from classical ML approaches for time series data. The classical featurization approach (see Methods for details) performed well (Figure ); however, its resource demands-in terms of memory footprint and processing delays-made it unsuitable for on-the-fly deployment in the Cloud Lab. Therefore, we transitioned to an end-to-end Deep Learning approach utilizing a 1D convolutional neural network (CNN) coupled with automatic architecture and hyperparameter optimization .