Video Highlights: ML System Design for Continuous Experimentation

While ML model development is a challenging process, the management of these models becomes even more complex once they’re in production. Shifting data distributions, upstream pipeline failures, and model predictions impacting the very data set they’re trained on can create thorny feedback loops between development and production.