Model Assertions for Monitoring and Improving ML Models

Daniel Kang, Deepti Raghavan, Peter Bailis +1

ML models are increasingly deployed in settings with real world interactions such as vehicles, but unfortunately, these models can fail in systematic ways. To prevent errors, ML en…