Tutorials Overview

Learn how to deploy a full MLOps infrastructure using deployml.

GCP Cloud Run

The primary supported deployment target is GCP using Cloud Run — a serverless architecture that scales automatically and is cost-effective for academic use.

Get Started with GCP Cloud Run →

End-to-End Example

Once deployed, walk through a complete MLOps workflow using a synthetic housing price dataset — training, registration, serving, drift monitoring, and Grafana dashboards.

End-to-End Example →

Kubernetes: local minikube and GKE

Prefer a Kubernetes cluster over Cloud Run? deployml can run the MLflow and FastAPI stack on a local minikube cluster for offline testing, or on GKE. MLflow keeps its data on a PersistentVolumeClaim so experiments survive pod restarts. The commands are minikube-init / minikube-deploy, mlflow-init / mlflow-deploy, and gke-cluster-create, gke-init, gke-deploy / gke-apply, gke-destroy.

GKE flow notes → and the CLI Commands reference.

Quick Reference

# 1. Check dependencies
deployml doctor

# 2. Enable GCP APIs
deployml init --provider gcp --project-id YOUR_GCP_PROJECT_ID

# 3. Build Docker images
deployml build-images --create-repo

# 4. Deploy the stack
deployml deploy --verbose

# 5. Get service URLs and write .env
deployml get-urls

# 6. Tear down when done
deployml destroy