The white paper explores using MLOps to monitor and audit production ML models, demonstrated through a simulated loan risk assessment system. It emphasizes continuous model delivery for rapid, automated deployment with built-in fairness checks, using Equal Opportunity metrics to address bias. The pipeline, managed via Jenkins and MLflow, ensures traceability from code to model performance. Production monitoring detects data drift and performance degradation through statistical tests, with dashboards tracking operational metrics, input drift, and output trends. This holistic MLOps approach ensures ethical, reliable, and sustainable ML integration into business processes.

Join for free to read