Abstract: Techniques and solutions are described for analyzing data collections to determine if they may be anomalous as compared with other data collections. For example, one or more values for data elements of a data collection may be unusually high or low, or may represent infrequently occurring values. Or, values of data elements in a data collection may not be anomalous when considered individually, but may be anomalous in combination. A machine learning model is trained with training data collections, where the training data collections include a plurality of data elements. An inference data collection, also having the data elements of the training data collections, is analyzed using the trained machine learning model to provide an anomaly score. The anomaly score can be based at least in part on feature anomaly scores, which indicate anomality of individual data elements of the inference data collection.