Insurance
Building an enterprise MLOps factory for Insurance
Building an enterprise MLOps factory for Insurance
An insurance company partnered with Marionete to standardise machine-learning delivery across its UK and Ireland businesses, creating a secure, scalable and governed MLOps factory that accelerated time to value sixfold.
The solution
Marionete first assessed the insurers MLOps landscape and defined a roadmap for an improved enterprise framework. We then worked alongside their data-science, engineering and platform teams to implement a common MLOps factory on Databricks and Azure DevOps.
The solution provides a repeatable path from development through staging to production. A modular framework and shared pipeline layer automate the machine-learning lifecycle, including data preparation, training, validation, deployment, serving and monitoring. Data scientists retain the flexibility to use custom code where needed, while configuration-driven workflows make common use cases faster and easier to deliver.
Security scanning, automated testing, model governance, human approval controls, observability and cost monitoring are brought into a single portfolio-management approach. Improvements to the common platform can benefit every participating team and use case.
Business impact
1.3 to 3.8+ MLOps maturity | 6x faster Time to value |
99% reduction Custom ML codebases | 4 teams / 2 units Platform adoption |
- Standardised MLOps across the insurer.
- Established a governed path to production.
- Reduced effort through reusable components and pipelines.
- Strengthened security, quality and auditability.
- Improved model performance and cost monitoring.
- Built internal capability through knowledge transfer.
Main features
- Enterprise MLOps factory: A common architecture for building, validating, deploying, serving and monitoring machine-learning models
- Flexible framework: Reusable data and model contracts combined with configuration-driven workflows and support for custom code.
- Automated CI/CD: Shared Azure DevOps pipelines for testing, security scanning, controlled promotion and deployment across development, staging and production.
- Governed model lifecycle: Model registry, release workflows, challenger and champion aliases, traceability and human approval controls.
- Feature management: A governed feature-store pattern with lineage, data-quality validation and integrated orchestration.
- Monitoring and FinOps: Consistent monitoring for model drift, performance, availability and infrastructure cost.
- Enablement and handover: Documentation, shared patterns and practical support for internal teams adopting the platform.