Databricks supports featurization with deep learning models. Pre-trained deep learning models may be used to compute features for use in other downstream models. Databricks supports featurization at scale, distributing the computation across a cluster. You can perform featurization with any of the deep learning libraries included in Databricks Runtime ML, including TensorFlow, PyTorch, and Keras.

Databricks also supports transfer learning, a technique closely related to featurization. Transfer learning allows you to reuse knowledge from one problem domain in a related domain. Featurization is itself a simple and powerful method for transfer learning: computing features using a pre-trained deep learning model transfers knowledge about good features from the original domain.