In string theory, we face a gigantic number of backgrounds, each of which comes with different implications for particle physics and cosmology. On top of this, every backgrounds has a huge number of possible vacua or near-vacua. We describe the computational complexity of the challenges associated with both finding a viable background and finding vacua for this background, and apply machine learning to study a small subset of them.