Database Management Systems (DBMSs) are the backbone for managing large volumes of data efficiently in the cloud. For providing high performance, many of the most complex DBMS components such as query optimizers or schedulers involve solving non-trivial problems. To tackle such problems, recent work has outlined a new direction of so-called learned DBMSs where core parts of DBMSs are being replaced by machine learning (ML) models which have shown to provide significant performance benefits. However, a major drawback of the current approaches to enabling learned DBMS components is that they not only cause very high overhead for training an ML model to replace a DBMS component but that the overhead occurs repeatedly which renders these approaches far from practical. In the first part talk, I will present our vision of zero-shot learned DBMSs to tackle these issues. In the second part, I will then outline very recent work on ML-augmented DBMSs to extend DBMS with new capabilities such as seamless querying of multimodal data which is composed of tables, text, and images.