Manual operations and growing regulatory requirements
Manual data preprocessing and model management limited productivity and reproducibility across the AI lifecycle. Fragmented development and production environments also made it difficult to standardize models and manage them consistently at scale.
As regulatory and security requirements in financial services continued to increase, the organization needed an integrated MLOps environment that could improve operational reliability while providing the traceability required for model governance and audits.
Building end-to-end MLOps on Runway while migrating legacy models
We implemented a new MLOps environment based on Runway within the organization’s Kubernetes infrastructure, while simultaneously migrating and refactoring existing production models.
The platform standardizes and automates the AI lifecycle from data processing and model development to deployment and operations. Customer reports are automatically generated and integrated with existing Excel templates, while continuous model management automates performance monitoring and retraining.
32% above target inference performance with production-grade reliability
The new environment achieved inference performance 32% above the target. A no-code model simulation environment also enables users to generate and validate credit scores without modifying code.
By combining standardized model operations, continuous monitoring, and governance capabilities, the organization established a secure and scalable MLOps environment designed to meet the operational and regulatory requirements of financial services.
