Practical Solutions for Annoying Machine Learning Problems

Solving problems in the real world with machine learning can be challenging: data from real processes doesn't usually behave like the data from example use cases of ML methods! This talk, designed for applied machine learning practitioners (in anything from business to science to social research), will inspire hope: many challenges of real and messy data can be overcome using straightforward analysis of that data! Alyssa will cover a robust way to add error bars to any number of complex metrics, a strategy for monitoring models in production when you can't always observe an outcome, and a way to plainly explain the decisions made by black-box models.