ML System Design for Continuous Experimentation
Shifting data distributions, upstream pipeline failures, and model predictions impacting the very dataset they're trained on can create thorny feedback loops between development and production.
In this webinar, we will
Examine some naive ML workflows that don't take the development-production feedback loop into account and explore why they break down.