Unified Machine Learning Platform
Architected an enterprise-grade MLOps platform that transformed the model development lifecycle for over 360 data scientists, reducing average deployment time from 8 weeks to under 1 week.
Project Overview
At USAA, I architected a unified, enterprise-grade Machine Learning Platform that seamlessly integrated heterogeneous technologies—including Python, R, SAS, and H2O—with core MLOps principles such as DevOps automation, model versioning, and robust governance.
The platform featured distinct architectural patterns, including event-driven pipelines for automated model retraining and CI/CD pipelines tailored for ML artifacts. I also introduced standardized model lineage tracking and auditability to ensure regulatory compliance and reproducibility.
Key Features
- Accelerated model deployment velocity by over 85%.
- Reduced operational overhead through automation.
- Established a consistent, scalable, and auditable MLOps framework.
- Supported diverse use cases across risk, fraud, marketing, and customer analytics.
Technologies Used
Project Gallery
Project Details
Client
USAA (Internal)
Timeline
2019 - 2021
Role
Platform Architect
More Projects
© 2025 Muthu. All rights reserved.