Overview
Durga Analytics specializes in productionizing AI models through modular, secure, and scalable engineering practices. We enable seamless transition from experimentation to enterprise-grade deployment.
Key Capabilities
- Model training orchestration and ML pipelines
- Model deployment using containers, APIs, and microservices
- Model versioning, governance, and rollback strategies
- A/B testing, performance monitoring, and model drift detection
- ML workflow automation and CI/CD for ML
Engineering Blueprint
1. Pipeline Design: Define ML workflows, training & validation triggers
2. Deployment: Serve models via REST, gRPC, or batch interfaces
3. Integration: Connect models with business apps, databases, and APIs
4. Monitoring: Track metrics, latency, and data integrity in production
Benefits
- Shorter cycle from model development to deployment
- Improved reliability, traceability, and compliance
- Seamless collaboration between data science and engineering
- Faster scaling across use cases with reusable components
Client Outcomes
- Deployed 30+ ML models to production in under 2 months for a BFSI client
- Built CI/CD-based ML platform using Kubernetes and MLflow for a telecom provider
- Achieved 40% reduction in model latency with API-first AI platform for a logistics firm
