Complete CI/CD Pipeline
Introduction
Now we put it all together: a complete pipeline that builds a Docker image, pushes it
to a registry, and deploys it to Kubernetes. This is the pattern you'll use for
deploying ML models and services.
Why This Matters
This is the culmination of Phase 2. You've learned:
- Docker (containerizing applications)
- Kubernetes (running containers at scale)
- Terraform (provisioning infrastructure)
- GitHub Actions (automating workflows)
Now you combine them into a real deployment pipeline. After this section, you'll be
able to push code and have it automatically deployed to Kubernetes — the same pattern
used by production ML systems worldwide.
Tasks
Hint: kubectl set image deployment/myapp myapp=<new-image-tag>
Hint: Use environment protection rules in GitHub