CI/CD

Technical terms
About 1 min read

A software development automation framework that continuously integrates (CI) code changes and immediately deploys (CD) them to production after automated validation. It is an essential methodology in modern development to ensure both quality and speed by detecting errors early and shortening deployment cycles.

Also known as
Continuous Integration/Continuous DeploymentContinuous Integration/Continuous Deployment

Detailed explanation

CI/CD is a core practice that automates the entire software development lifecycle to improve code quality and deployment speed. Continuous Integration (CI) regularly integrates code submitted by developers and runs automated builds and tests to detect defects early. Continuous Deployment (CD) immediately deploys validated code to the production environment or keeps it in a deployable state. In AI projects specifically, it serves as the foundation for building MLOps, which involves data validation and model retraining. Recently, it has evolved into intelligent automation where AI predicts pipeline errors or optimizes test cases. It is implemented using tools like GitHub Actions, GitLab CI, and Jenkins, minimizing human error from manual operations.

Why it matters in tool selection

Unlike traditional software, AI services face frequent changes in data and models, making deployment risks higher. Without automated CI/CD, managing updates manually prevents rapid response to model degradation or data drift. Therefore, choosing tools that are highly compatible with AI infrastructure and can efficiently allocate GPU computing resources determines the success of a project.

What to check for AI projects

  • GPU Runner Support: Is it easy to integrate GPU instances for model training and validation?
  • Data Version Control Integration: Does the pipeline connect seamlessly with AI-specific tools like DVC or MLflow?
  • Deployment Strategy Support: Can it minimize risk during model updates using Canary or Blue-Green deployments?
  • Security and Compliance: Are features like API key exposure prevention in code and vulnerability scanning (DevSecOps) built-in?

Real-world example

When a developer pushes new data preprocessing logic to GitHub, GitHub Actions is triggered to automatically run unit tests. Once the tests pass, a Docker image is built, and the model serving endpoint in a cloud-based Kubernetes cluster can be automatically updated.

Confusing Terms

CI (Continuous Integration)

The phase that maintains codebase consistency through code integration and automated building/testing.

CD (Continuous Deployment)

The final phase that automatically deploys tested outputs to the production environment without human intervention.

CT (Continuous Training)

An MLOps-specific concept representing a pipeline that automatically retrains models whenever new data is ingested.

Related terms

MLOpsDevOps