If your machine learning models are deployed manually, take weeks to move from training to production, or have no monitoring in place — your ML investment is at risk.

At Amazon Web Services, I led a project that reduced model deployment time from two weeks to two days. That same architecture pattern — versioned models, automated promotion, drift detection, and full observability — is what I bring to your stack.

When I deliver this engagement, you get a complete MLOps pipeline: model versioning, training triggers, drift monitoring, CI/CD for model promotion, and Prometheus and Grafana dashboards so you can see exactly what is happening at all times.

Model versioning and registry (AWS SageMaker Model Registry or MLflow)

Automated training trigger pipelines

Model promotion CI/CD (dev → staging → production gates)

Drift detection with configurable alerting thresholds

EKS or SageMaker deployment configuration

Prometheus metrics instrumentation and Grafana dashboards

Runbook for operating the pipeline post-delivery