AiVibeCI/CD Pipeline
A CI/CD pipeline automates the path from code commit to release, using continuous integration to build and test every change and continuous delivery or deployment to package and ship validated changes to production.
A CI/CD pipeline is an automated sequence of steps that takes code changes from a version control commit to a tested, releasable or deployed build. Continuous integration (CI) means developers merge changes frequently into a shared branch, where each change is automatically built and tested. Continuous delivery keeps the software in a releasable state with deployment triggered by approval, while continuous deployment releases every change that passes the pipeline to production automatically.
Typical stages are build, unit and integration tests, static code analysis, security scanning, packaging into artefacts or container images, deployment to test environments and promotion to production. Tools include GitHub Actions, GitLab CI/CD, Jenkins, Azure Pipelines and AWS CodePipeline. CI/CD shortens feedback loops, reduces manual release errors and makes small, frequent releases routine.
Pipelines hold credentials for production systems, so they are a high-value target: secrets should be scoped and short-lived, build environments isolated and pipeline definitions reviewed like code. Integrating SAST, dependency and container scanning gives early security feedback. Delivery performance is often measured with the DORA metrics, which include deployment frequency, lead time for changes, change failure rate and time to restore service.
Key points
- Continuous integration builds and tests every change merged to a shared branch
- Continuous delivery needs approval to release; continuous deployment releases automatically
- Stages typically include build, test, security scanning, packaging and deployment
- Delivery performance is often tracked with the DORA metrics
Where AiVibe comes in
AiVibe Software Services delivers cloud solutions on AWS, Microsoft Azure, Google Cloud or on-premise, together with cloud security, legacy modernisation, data analytics and AI and machine learning services.
Related terms
- DevOpsCloud & AI Infrastructure
- GitOpsCloud & AI Infrastructure
- Infrastructure as Code (IaC)Cloud & AI Infrastructure
- Container ImageCloud & AI Infrastructure
- Blue-Green DeploymentCloud & AI Infrastructure
- Canary ReleaseCloud & AI Infrastructure