model-observability

Implement ML observability for production systems. Covers prediction logging, model explainability (SHAP, LIME, Integrated Gradients, Anchors, counterfactuals), feature attribution, OpenTelemetry integration for ML services, distributed tracing across pipeline components, structured logging, debugging mispredictions, slice-based analysis by cohort/segment, fairness and bias detection, model interpretability dashboards, root cause analysis, data lineage visualization, prediction audit trails, Arize/Fiddler/WhyLabs integration, and custom observability metrics. Use when debugging models, explaining predictions, setting up tracing, or building interpretability dashboards.

Version

1.0

License

Apache-2.0