ML observability entails tracking the performance of ML system across its lifecycle, right from when it is being built, to pre and post-production. ML observability brings a proactive approach to investigating model issues and highlights the root cause of the problem. Observability covers a larger scope compared to ML monitoring - it understands why the problem exists, and the best way to resolve it.