Production MLOps for Credit Risk Models
Built an end-to-end MLOps platform that took credit risk models from notebook to production in days, with full governance and drift monitoring.
Data scientists built strong models that took months to deploy, with no reproducibility, lineage, or monitoring — an unacceptable risk for credit decisions.
We delivered a feature store, model registry, automated evaluation, and a governed promotion pipeline, embedding model risk controls directly into the workflow.
Feast feature store, MLflow registry, Ray for distributed training, automated evaluation gates in CI, and real-time drift monitoring feeding a model-risk dashboard.
Time-to-production dropped 85%, every model gained complete lineage, and continuous drift monitoring gave risk teams the assurance they needed to expand model usage.