Stripe processes billions of dollars a year for businesses around the world. To protect its users from fraud, Stripe employs machine learning to detect potentially fraudulent transactions. In this talk, I'll describe how we bootstrapped this system and some of the most important aspects of industrial machine learning. We'll talk about how to choose, train, and evaluate models, how to bridge the gap between training and production systems, and how to address common pitfalls using the problem of fraud detection as our motivation. By the end of the talk, you should be familiar with many of the core concepts in practical machine learning: regression, random forests, training and validation sets, ROC and AUC curves, and production scoring, monitoring, and evaluation.