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.
Alyssa is a machine learning engineer at Stripe, where she builds models to prevent online credit card fraud. Prior to joining Stripe, she completed a PhD in biostatistics before falling in love with programming at the Recurse Center.
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