Explore the role of data and models in deep learning-based image reconstruction through this insightful lecture by Reinhard Heckel from the Technical University of Munich. Delve into the impact of model size and training data on performance, particularly in accelerated magnetic resonance imaging. Examine the robustness of deep learning methods compared to classical reconstruction techniques, and investigate their performance under distribution shifts. Discover strategies to improve out-of-distribution performance, including the use of diverse training data and test-time-training. Gain valuable insights into scaling challenges, dataset sizes in imaging tasks, and the behavior of various reconstruction methods such as U-net-based denoising and Swin transformer-based denoising.