Fiddler AI Observability is a platform designed to provide visibility and monitoring capabilities for machine learning models throughout their lifecycle. The platform supports MLOps practices by enabling teams to track model performance, detect drift, and analyze model behaviors from training through production deployment. The platform offers continuous monitoring capabilities for both structured and unstructured data models, including text and image models. It tracks model health metrics including performance degradation, data drift, data integrity issues, and model bias. Real-time alerting notifies teams of changes in model behavior. The platform provides explainability features that generate human-readable explanations of model predictions at both global and local levels. Users can analyze how different features contribute to model outcomes and compare feature values across predictions. The system supports custom explainers for specific AI projects. Analytics capabilities include pre-built performance metrics for model validation, root cause analysis tools for investigating performance issues, and data slicing functionality to identify underperforming segments. Dashboards display feature impact, correlation, and distribution information. The platform includes fairness assessment tools that check for bias and intersectional unfairness at any stage of the ML lifecycle. Built-in fairness metrics include disparate impact, group benefit, equal opportunity, and demographic parity. Model governance features provide audit trails, compliance tracking, and fine-grained access controls. The platform supports regulatory requirements including GDPR, HIPAA, and AI governance guidelines.