Deploy MLflow to Kubernetes
Introduction
MLflow will be the experiment tracking and model registry for your platform. You'll
deploy it to your EKS cluster, configured with S3 for artifact storage and RDS
for the tracking database.
Why This Matters
Running MLflow locally was fine for learning, but for a real platform:
- Multiple team members need access
- Artifacts need durable storage (S3)
- Metadata needs a real database (RDS)
- The server needs to be always-on and accessible
Deploying MLflow to Kubernetes teaches you patterns you'll use for other services:
- Kubernetes Deployments and Services
- Environment configuration with ConfigMaps and Secrets
- Connecting to RDS from within the cluster
- Exposing services externally (Ingress or LoadBalancer)
Tasks
Hint: Use an auth proxy like oauth2-proxy or simple nginx basic auth