Overview of what will be covered and the lab scenario
Lab: Lab environment setup and authentication
GitLab Runners Deep Dive
Runner scaling
Multi-zone / multi-region runner availability
Configuring a Kubernetes executor
Services
Lab: Adding and configuring a runner
Optimizing Pipeline Builds
Pipeline efficiency
Caching
Dependency Proxy
Artifacts
Lab: Caching dependencies
Optimizing Pipeline Testing
Fail fast testing
Unit test reports
Parallelism/test splitting
Lab: Unit test reporting and parallelization
Revisiting Best Practices
Extends
Hidden Jobs
Anchors
Aliases
Map merging
Reference tags
Lab: Analyzing a pipeline for efficiency optimization
Configurations for Complex Processes
Workflow
Merge trains
Multi-project pipelines, including sharing artifacts and variables
Lab: Merge trains and merge conflicts
Review Stage
Review apps
Lab: Create a review app
Deployment Strategies
Feature flags
Blue/Green Deployments (GitLab Canary)
Managing multiple environments
Lab: Using feature flags
Advanced Troubleshooting)
Addressing troubleshooting for more complex use cases
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