ML Ops Pipeline
End-to-end MLOps infrastructure for training and deploying models at scale
Infrastructure-as-code templates and Kubernetes manifests for scalable ML operations.
Components
- Automated model training pipelines
- Feature store with versioning
- Model registry with MLflow
- A/B testing and canary deployments