ML observability is an essential part of the MLOps toolchain that helps teams automatically surface and resolve model performance problems before they negatively impact business results. It enables retraining workflows by tracking prediction drift, concept drift, and data/feature drift to know immediately if a model is drifting due to changes between the current and reference distributions. This allows for more efficient model updates and minimizes the risk of introducing new biases or issues. Model version control provides side-by-side analysis of how each version of a model performs, enabling teams to evaluate the efficacy of their optimizations and retraining efforts. Deprecating models is crucial to prevent regression errors and ensure reliable ML environments in production. Fairness checks and bias tracing are critical for determining whether models are exhibiting algorithmic bias, while data labeling can help detect changes in new patterns that emerge in unstructured data. By implementing ML observability best practices, teams can ensure a solid foundation for future success in MLOps.