I will build azure mlops pipeline with mlflow model registry and drift detection

Struggling to move ML models from notebooks to production? I will build a production-ready MLOps platform on Azure ML and Kubernetes the same setup I built for Publicis Sapient, Novelis, and Micron.

WHY CHOOSE ME?

6+ years as a Senior DevOps and MLOps Engineer. My work reduced model release cycles from weeks to days and cut degradation incidents by 60%.

WHAT YOU GET:

- Automated ML training pipelines on Azure ML

- MLflow experiment tracking and model registry

- Data drift detection with auto retraining triggers

- FastAPI inference API on AKS with HPA autoscaling

- CI/CD pipeline via Azure DevOps or GitHub Actions

- Grafana dashboards for model and pipeline health

STACK:

Azure ML, MLflow, AKS, Docker, FastAPI, Prometheus, Grafana, Terraform, Helm, GitHub Actions

Message me before ordering so I can recommend the right package for your needs.