Overview of DBAI@NeurIPS'21

After two decades of in-RDBMS machine learning research and implementations, database systems have not made a compelling case for data scientists to move their workflows there. A transition phase is currently under way, where the database community with all the experience of the past is looking for crucial features, such as data versioning and data governance, that would make DBMSes attractive to data scientists, and where the definition of in-RDBMS machine learning becomes less rigid with the adoption of data lakes and the interoperability with systems like TensorFlow and open formats like ONNX. Overall, we are very happy with the content of the 1st DBAI, as this included insightful presentations and a constructive panel discussion. I'd like to sincerely thank my fellow organizers (Nikolaos Vasilogou, Parisa Kordjamshidi, Maximilian Schleich, Kirk Pruhs and Zenna Tavares), the PC members, the speakers and panelists, the sponsors, the volunteers and last but not least the authors and attendees for contributing each in his/her own way in making DBAI'21 a successful workshop. I really hope we will have the opportunity to organize another DBAI soon.