This talk describes Snorkel, a software system whose goal is to make routine machine learning tasks dramatically easier. Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets for a user’s task. In Snorkel, a user implicitly defines large training sets by writing simple programs that create labeled data, instead of tediously hand-labeling individual data items. In turn, this allows users to incorporate many sources of training data, some of low quality, to build high-quality models. This talk will describe how Snorkel changes the way users program machine learning models. A key technical challenge in Snorkel is combining heuristic training data that may have uneven and unknown quality and an unknown correlation structure. This talk will explain the underlying theory, including methods to learn both the parameters and structure of generative models without labeled data. Additionally we’ll describe our recent experiences with hackathons, which suggest the Snorkel approach may allow a broader set of users to train machine learning models and do so more easily than previous approaches. Snorkel is being used by scientists in areas including genomics and drug repurposing, by a number of companies involved in various forms of search, and by law enforcement in the fight against human trafficking. Snorkel is open source on github. Technical blog posts and tutorials are available at Snorkel.Stanford.edu.