This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, and application of the model to another observatory and quantify how the models respond to instrument outages.