In this episode of MLOps Live, Sabine and Stephen are joined by Danny Leybzon, MLOps Architect at WhyLabs. They examine the differences between monitoring and observability in machine learning models for production and methods for efficient implementation and development. Observability in MLOps is a holistic and comprehensive way to gain insights into the behavior, data, and performance of a machine learning model throughout its lifespan. It allows for detailed root cause analysis of ML model predictions and aids in the development of responsible models. Although ML monitoring and observability appear to be similar, Danny points out that monitoring is a continuous system that prompts you when there is a problem. Whereas observability refers to the larger picture, a human-in-the-loop root cause analysis system that allows you to figure out what the problem is and then solve it. Danny further discusses the unique features of WhyLabs in comparison to other conventional monitoring solutions, such as customizable and opinionated self-serve capabilities that allow users to pick particular metrics to track, especially in the absence of ground truth.