We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well. Here we assemble 15 multimodal data tables that each contain some text fields and stem from a real business application. Over this benchmark, we evaluate numerous multimodal AutoML strategies, including a standard two-stage approach where NLP is used to featurize the text such that AutoML for tabular data can then be applied. We propose various practically superior strategies based on multimodal adaptations of Transformer networks and stack ensembling of these networks with classical tabular models. Beyond performing the best in our benchmark, our proposed (fully automated) methodology manages to rank 1st place (against human data scientists) when fit to the raw tabular/text data in two MachineHack prediction competitions and 2nd place (out of 2380 teams) in Kaggle’s Mercari Price Suggestion Challenge.