Client: A leading neobank with 5M+ customers
Industry: Finance & Insurance
Challenge:
The client used multiple credit scoring models, each deployed in isolated environments. Lack of standardization meant maintenance costs were high and production incidents were frequent. Regulatory compliance required model audit logs, but manual tracking was error-prone.

Solution:
We deployed a centralized MLOps platform that included:

• Standardized model packaging using Docker
• Automated validation against regulatory compliance rules before deployment
• Kubernetes-based scaling for high-traffic inference requests
• Model registry for unified tracking and version control

Results:

• Reduced model maintenance costs by 40%
• Model deployment frequency increased by 3x without service downtime
• Achieved full compliance with regulatory audit requirements

Key MLOps Features Used:
✅ Model registry & governance
✅ Automated compliance checks
✅ Scalable inference serving