  Machine Learning (ML) is changing DBs as many DB components are being
replaced by ML models. One open problem in this setting is how to update such
ML models in the presence of data updates. We start this investigation focusing
on data insertions (dominating updates in analytical DBs). We study how to
update neural network (NN) models when new data follows a different
distribution (a.k.a. it is "out-of-distribution" -- OOD), rendering
previously-trained NNs inaccurate. A requirement in our problem setting is that
learned DB components should ensure high accuracy for tasks on old and new data
(e.g., for approximate query processing (AQP), cardinality estimation (CE),
synthetic data generation (DG), etc.). This paper proposes a novel updatability
framework (DDUp). DDUp can provide updatability for different learned DB system
components, even based on different NNs, without the high costs to retrain the
NNs from scratch. DDUp entails two components: First, a novel, efficient, and
principled statistical-testing approach to detect OOD data. Second, a novel
model updating approach, grounded on the principles of transfer learning with
knowledge distillation, to update learned models efficiently, while still
ensuring high accuracy. We develop and showcase DDUp's applicability for three
different learned DB components, AQP, CE, and DG, each employing a different
type of NN. Detailed experimental evaluation using real and benchmark datasets
for AQP, CE, and DG detail DDUp's performance advantages.
