Statistics and machine learning are part of many new systems, but have been described as the “high interest credit card of technical debt.” Is it possible to make statistical inference broadly accessible to non-statistician programmers without sacrificing mathematical rigor or inference quality? BayesDB is a system that enables users to query the probably implications of their data as directly as SQL enables them to query the data itself. This presentation focuses on BQL, an SQL-like query managed for Bayesian data analysis that answers queries by averaging over an implicit space of probabilistic models. The demonstration focuses on analysis of a public database of Earth satellites.