It’s hard to overstate the societal and business impact that machine learning has had in the past decade and will continue to have in decades to come. It is also hard to overstate the risk; machine learning models fail frequently and such failures can have dire consequences. Thus, any organization that relies on machine learning in critical decision making is now building strategies and engineering paradigms that ensure machine learning integrity. In this panel, hear from industry leaders who understand the business implications of model failure and are implementing practices to eliminate it.