AI Platform Migration
Modernizing legacy machine learning systems into scalable cloud-native architectures.
Overview
Guided a fintech enterprise in migrating their legacy machine learning systems to a cloud-native AI pipeline. The transformation involved containerization, automated model training pipelines, and integration with Kubernetes-based orchestration systems to achieve horizontal scalability and enhanced model performance.
Key Achievements
- 300% increase in model training speed with GPU-accelerated compute.
- 99.99% system availability with auto-scaling and fault-tolerant design.
- End-to-end ML pipeline with real-time monitoring and logging.
Technical Stack
Technical Implementation
Containerization
Containerized legacy models using Docker, enabling seamless deployment across hybrid cloud environments.
Model Optimization
Optimized machine learning models using Quant, achieving 40% reduction in inference latency without loss in accuracy.
How it Worked
- 1 Assessed legacy systems with performance bottlenecks
- 2 Designed cloud-native architecture with Kubernetes and MLflow tracking
- 3 Deployed optimized models with CI/CD pipelines