Kubernetes ConfigMaps, Secrets, and Persistent Volumes for ML Models
Managing Application Configuration, Credentials, and Persistent Model Storage in Kubernetes
By Sandip Das · · 19 min read
💡
Hey — It's Sandip Das 👋
We should learn ConfigMaps, Secrets, and Persistent Volumes to separate application settings and credentials from container images while securely managing sensitive data.
These resources also provide reliable storage for ML models, datasets, and other artifacts across Pod restarts and deployments.
We should learn ConfigMaps, Secrets, and Persistent Volumes to separate application settings and credentials from container images while securely managing sensitive data.
These resources also provide reliable storage for ML models, datasets, and other artifacts across Pod restarts and deployments.
📚 Key Learnings
- Why config and storage break ML deployments
- ConfigMaps: the config surface
- Consuming a ConfigMap: env vars vs files
- Secrets: credentials done properly
- Secret hardening and AWS integration
- Persistent Volumes, PVCs, StorageClasses
- Choosing storage for model artifacts
- Hands-on: wire it into iris-mlops
- Verifying and troubleshooting
🧠 Learn here
Why config and storage break ML deployments
On Day 23 you shipped the iris classifier to Kubernetes with a Deployment and a Servic
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Community, Kubernetes