GitLab CI Training — Raffael Hühnerschulte

What We Cover

Source to production, all in one platform

Day 1

GitLab CI architecture: Pipelines, stages, jobs, runners, artifacts — how the system fits together
.gitlab-ci.yml: stages, script, rules, needs, extends, include — the full YAML syntax
Job templates with extends and !reference: Reducing duplication across many job definitions
Rules vs only/except: Modern conditional job control — when to use rules and how to write them
Artifacts: Passing files between jobs, browsing artifact archives, expiry policies
Cache: pip, npm, Maven — correct cache key strategies for fast pipelines
GitLab Container Registry: Building and pushing images in CI with the built-in registry
Auto DevOps and when to override it: Understanding the defaults and customizing for your project

Day 2

Downstream pipelines: trigger, parent-child pipelines — coordinating multi-project deployments
GitLab Runner configuration: Executor types (Docker, Kubernetes, shell) and when to use each
Kubernetes executor: Runner as a Kubernetes Deployment, pod templates, resource requests
Environments and deployments: Tracking deployment history and rollbacks in GitLab
GitLab Agent for Kubernetes (gla): Pull-based cluster integration without exposing the API server
Secrets management: GitLab CI variables, masked variables, HashiCorp Vault integration
Compliance pipelines: Enforcing security jobs across all projects in a group
DAST, SAST, and dependency scanning: Built-in security scanning templates and interpreting results

Learning Outcomes

What your team walks away with

End-to-end GitLab CI practitioners who can design, optimize, and maintain pipelines that cover build, test, security scan, and Kubernetes deployment.

Can be combined with the GitHub Actions training for a CI/CD platform comparison session.