Atopic dermatitis (AD), also known as eczema, is one of the most common chronic skin diseases. AD severity is primarily evaluated based on visual inspections by clinicians, but is subjective and has large inter-and intra-observer variability in many clinical study settings. To aid the standardisation and automating the evaluation of AD severity, this paper introduces a CNN computer vision pipeline, EczemaNet, that first detects areas of AD from photographs and then makes prob-abilistic predictions on the severity of the disease. EczemaNet combines transfer and multitask learning, ordinal classification, and ensembling over crops to make its final predictions. We test EczemaNet using a set of images acquired in a published clinical trial, and demonstrate low RMSE with well-calibrated prediction intervals. We show the effectiveness of using CNNs for non-neoplastic dermatological diseases with a medium-size dataset, and their potential for more efficiently and objectively evaluating AD severity, which has greater clinical relevance than mere classification.