Overview
Durga Analytics empowers businesses to deploy, monitor, and scale machine learning models reliably using robust MLOps pipelines built with open-source and cloud-native technologies.
Key Services
- CI/CD pipelines for ML model training and deployment
- Model versioning, rollback, and governance setup
- Model monitoring, performance tracking, and drift detection
- Automated retraining, testing, and validation workflows
- Secure, scalable MLOps platforms on AWS, Azure, GCP, or hybrid clouds
MLOps Lifecycle Framework
1. Develop: Standardize experimentation with reproducible ML pipelines
2. Deploy: Automate releases using GitOps, containers, and workflow triggers
3. Monitor: Track predictions, usage, and model quality in real-time
4. Optimize: Enable continuous improvement and cost visibility across models
Benefits
- Faster and safer model rollouts to production
- Higher model performance and business trust
- Lower operational overhead with automation
- Unified view of model health and compliance
Client Success
- Set up a centralized MLOps platform using MLflow and Kubernetes for a global bank
- Enabled model traceability and automated testing for a med-tech AI startup
- Reduced ML deployment time from weeks to hours for a fintech firm
