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Credit Scoring Batch API
Production FastAPI microservice for batch ingestion of 18+ financial and behavioral data domains, powering credit scoring for 5 Ethiopian partner banks via Feast Feature Store and ML pipelines
5–10 pod autoscaling under load
Performance
99.9% pipeline reliability
Improvement
Serving 5 Ethiopian partner banks
Impact
Tech Stack
FastAPIMongoDBFeast Feature StoreKubernetes (EKS)DockerAWSPrometheusKubeflow PipelinesPythonPyMongoPandasGitHub Actions
Problem Statement
Five Ethiopian partner banks needed a reliable, scalable API to ingest diverse financial and behavioral data — transactions, SMS, KYC, psychometrics, employment, demographics, invoices — for ML-driven credit scoring and fraud detection.
Technical Approach
Built a FastAPI microservice with 22+ REST endpoints across 18+ data domains, MongoDB backend with 25 collections, and Feast Feature Store integration for ML feature pipelines. Deployed on AWS EKS with Horizontal Pod Autoscaler (5–10 replicas), CI/CD via GitHub Actions, and Prometheus metrics for observability.
Key Results
- →22+ REST endpoints across 18+ data domains (SMS, KYC, transactions, psychometrics, employment, etc.)
- →Feast Feature Store integration enabling credit scoring and fraud detection ML models
- →AWS EKS deployment with HPA auto-scaling (5–10 pods) and zero-downtime rolling updates
- →CI/CD pipeline via GitHub Actions with automated ECR builds and EKS rollouts
- →Prometheus metrics for real-time monitoring of MongoDB ops, auth, and domain request rates