ML Observability is a platform that enables teams to analyze model degradation and identify the root cause of issues by connecting points across validation and production environments. This allows for a deeper understanding of the "why" behind performance changes, which is different from traditional model monitoring that focuses on aggregates and alerts. The platform provides features such as explainability insights, production feature data analysis, and distribution drift analysis to help teams troubleshoot and improve their models. By using ML Observability, teams can gain confidence in their models' performance, scale their operations, and gain a competitive advantage in the market.