CI/CD (Continuous Integration / Continuous Delivery)
Definition
CI/CD is the engineering practice of automatically building, testing, and deploying software every time code is committed to a version control system. Teams with mature CI/CD pipelines deploy to production 200x more frequently with 24x faster incident recovery than teams without automation, according to DORA research -- the most measured indicator of engineering organizational health.
Continuous Integration (CI) means every code commit triggers an automated build and test run, surfacing regressions within minutes. Continuous Delivery (CD) means every passing build is automatically deployable to production -- or automatically deployed, depending on your configuration.
CI/CD pipeline stages
- Build -- compile code, install dependencies, produce deployable artifact
- Test -- unit tests, integration tests, security scans, lint checks
- Staging deploy -- automatic deployment to a production-mirror environment
- Smoke test -- automated end-to-end tests against staging
- Production deploy -- manual gate or fully automated, depending on risk tolerance
CI/CD for AI systems
AI systems require additional pipeline stages: model version pinning, prompt regression tests (compare LLM output against a golden test set), and embedding pipeline validation. Without these, a model API version upgrade can silently degrade production AI quality.
Related terms
Microservices
Microservices is a software architecture pattern where an application is decomposed into small, independently deployable services that each own a single business capability and communicate over APIs or message queues. Netflix and Amazon migrated to microservices to enable thousands of engineers to deploy independently -- teams that adopt microservices report 60-80% reductions in deployment coupling.
DevOps
DevOps is the organizational and technical practice of unifying software development and IT operations teams around shared tooling, automation, and accountability for the full software delivery lifecycle -- from code commit through production monitoring. Organizations that adopt DevOps deploy software 46x more frequently and recover from incidents 96x faster than those that keep dev and ops siloed.
Containerization
Containerization is the packaging of application code, runtime, libraries, and configuration into a self-contained unit (a container) that runs identically across development, staging, and production environments. Docker containers start in under 2 seconds and use 10x less memory than virtual machines, making them the standard deployment unit for modern cloud-native applications.
Observability
Observability is the ability to understand the internal state of a software system from its external outputs -- logs, metrics, and traces -- without modifying the code to answer each new question. Teams with high observability resolve production incidents 3x faster and detect degradations before users report them, according to DORA and OpenTelemetry benchmark data.
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