Continuous Deployment: Automating ML Model Deployment

Automate ML Model Deployment to Kubernetes with GitOps, ArgoCD, and Helm.

By Sandip Das · · 15 min read

Continuous Deployment: Automating ML Model Deployment
💡
Hey — It's Sandip Das 👋

Learn continuous deployment to automate model releases, reduce manual errors, and keep Kubernetes deployments consistent with Git.

GitOps tools like ArgoCD make changes traceable and help teams reliably deploy, update, and roll back ML services.

📚 Key Learnings

  • Why pushing kubectl apply from a CI runner stops scaling, and what pulling fixes
  • The four GitOps principles, and the one that actually does the work: continuous reconciliation
  • Argo CD installed on minikube, with an Application pointed at your Day 26 Helm chart
  • A CI job that writes the new image digest into a config repo, so deploys happen without anyone typing a command
  • Where GitOps gets awkward for ML, and what you put in Git when the artifact is a 400 MB model

🧠 Learn here

By the end of

Read the full article on learnxops.com.

Topics: 100 Days of MLOps, MLOps, ML, GitOps, ArgoCD