My group aims to dramatically increase the efficiency of data-intensive systems and democratize data science by enabling a broader range of users to unfold the potential of (their) data through the development of a new generation of algorithms and systems. This entails exploring how we can build systems to better support the recent advances in machine learning (Systems for ML) and how we can leverage machine learning to improve systems (ML for Systems). For example, with our work on SageDB we started to explore how we can enhance or even replace core systems components using machine learning models and early results suggest, that we can improve the state-of-the-art by more than an order-of-magnitude in performance. On the other hand, with Northstar we are exploring new user interfaces and infrastructure to democratize data science by enabling visual, interactive, and assisted data exploration and model building. One particular focus of this work is to help all types of users to analyse data and build models faster, but also make data exploration and model building safer by automatically preventing the user from common pitfalls.