We have seen astonishing progress of machine learning research in the last years. Unfortunately, it is often dificult to translate this academic progress into deployable applications, due to the constraints and challenges imposed by production settings. In this talk, I will present some of my recent research in the area of data management for machine learning, which tackles these problems. Furthermore, I will put a special focus on three challenges that I see for building industrial-scale recommender systems. In particular, I will outline ideas on how to scale to datasets with billions of interactions, understand the impact of response latency on the performance of a deployed recommender system, and make the "right to be forgotten" a first-class citizen in real-world ML systems.