ML observability refers to tools and practices that help teams monitor and understand the performance of their machine learning (ML) models in real-world scenarios. This is particularly important as more teams adopt ML to streamline their businesses or turn impractical technologies into reality. The challenge lies in translating research lab models to production environments, where data and feature transformations can be inconsistent, leading to poor model performance. By applying evaluation stores and introspection techniques, teams can identify gaps in training data, detect underperforming model slices, compare model performances, validate models, and troubleshoot issues in real-time, ultimately improving their ML efforts.