Accelerating Time to Market for ML Models in the Insurance Industry

Business Context

A leading financial services company, was facing significant challenges with their existing machine learning infrastructure. Data scientists were struggling with fragmented tools, manual deployment processes, and lack of standardised workflows. The existing DataRobot platform required migration to a more scalable, cloud-native solution that could support diverse ML use cases while maintaining strict financial industry compliance requirements.

The company needed to accelerate their digital transformation initiatives while ensuring robust model governance, monitoring, and deployment capabilities across multiple business units.

Challenge

The existing ML ecosystem consisted of disparate tools and manual processes that created significant friction:

• Extended deployment cycles: Models took 2-3 months to move from development to production
• Limited scalability due to cost: DataRobot platform becoming costly when increasing number of models and complexity
• Compliance challenges: Lack of proper model governance and audit trails
• Resource inefficiency: Data scientists spending 60%+ time on infrastructure rather than model development
• Integration complexity: Difficulty connecting ML workflows with existing enterprise systems

Solution

Comprehensive SageMaker-based MLOps Platform

Melio AI delivered the solution involving designing and implementing an end-to-end MLOps platform on AWS SageMaker that addresses all aspects of the ML lifecycle:

Multi-Modal Deployment Architecture

• DataRobot Integration: Custom Docker containers and deployment patterns for seamless DataRobot model migration
• SageMaker Canvas: Low-code/no-code interface enabling business analysts to build models independently
• SageMaker Pipelines: Enterprise-grade CI/CD workflows with automated model build, validation, and deployment
• Flexible Inference Options: Real-time endpoints, batch transform jobs, and cross-account deployment patterns

Enterprise-Grade Infrastructure & Governance

• Secure VPC Design: Network isolation with private subnets and VPC endpoints for enterprise security
• IAM Framework: Multi-level user permissions and role-based access control with least privilege principles
• Infrastructure as Code: Terraform and CloudFormation templates for repeatable, version-controlled deployments
• SageMaker Domain: Centralised Studio environment with lifecycle management and shared resource optimisation

ML Lifecycle Management

• Model Templates: Pre-built ML profile templates for regression, classification, and DataRobot use cases
• Model Registry: Cross-account model versioning with automated approval workflows via GitHub and SageMaker
• Feature Engineering: SageMaker Pipeline templates with data lineage tracking and feature store integration options
• Repository Structure: Standardised ModelBuild, ModelDeploy, and ModelMonitor repositories for each project

Monitoring & Cost Optimisation

• Comprehensive Monitoring: Data drift detection, model performance monitoring, and endpoint observability dashboards
• Audit & Compliance: Complete audit trails via CloudTrail and CloudWatch for regulatory requirements
• Cost Tracking: Resource tagging strategy with budget alerts and detailed cost allocation across environments
• Automated Alerts: Proactive monitoring with configurable thresholds for model and infrastructure health