ML Observability: Bringing Transparency to Payments and Beyond

This post details Netflix's approach to ML observability, focusing on its application in payment processing. It introduces a framework for ML observability encompassing logging, monitoring, and explaining ML model decisions. Key contributions include defining a data schema for logging ML events, emphasizing stakeholder-focused online metrics for monitoring (e.g., traffic routing, regional performance), and detailing the use of SHAP values for model explainability to understand the 'why' behind ML decisions at both aggregate and individual levels. The post highlights the importance of transparency and trust for business stakeholders by translating technical ML performance into business outcomes.