MULTI-TASK MACHINE LEARNING WITH HETEROGENEOUS DATA

Embodiments of the disclosed technologies receive, for a first machine learning task, a first set of raw features arranged according to a first schema, and, for a second machine learning task, a second set of raw features arranged according to a second schema different than the first schema. A multi-task raw feature set is created by storing, in a data store arranged according to a common schema, the first and second sets of raw features. A common feature that is common to both the first and second sets of raw features is identified. A multi-task transformed feature set and a model bundle are created. The multi-task transformed feature set is separated into first and second sets of transformed features. The first and second sets of transformed features and the model bundle can be used to create a trained multi-task machine learning model.