Production MLOps for Credit Risk Models

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.