Course Wiki

CI/CD

Automated pipelines that test, build, and deploy ML code — GitHub Actions from MLOps and AI Dev Tools Zoomcamps.

CI/CD closes the best-practices module: every push to GitHub triggers a GitHub Actions workflow that lints and formats the code, runs unit tests, builds the Docker image, and — for releases — deploys it. The pipeline is defined as code in the repository, so the process is reviewable and reproducible.

AI Dev Tools Zoomcamp extends the same practice to full-stack apps: a workflow that runs backend and frontend tests (including Playwright end-to-end tests) and deploys to AWS only when everything passes. In both courses the principle is identical — no manual deployment steps between a tested commit and production.

Taught in

• AI Dev Tools Zoomcamp

  • Module 2: Build and Ship an AI-Assisted Full-Stack App
    • Build and Ship an AI-Assisted Full-Stack App
  • Module 3: Test, Containerize, and Deploy an AI-Assisted App
    • Test, Containerize, and Deploy an AI-Assisted App
  • Module 4: DevOps and Observability for AI-Built Apps
    • DevOps and Observability for AI-Built Apps
  • Module 5: Coding Agent Building Blocks: Reusable Skills and Specialized Subagents
    • Module 5 — Coding Agent Capabilities: MCP, Skills, Plugins, and Custom Agents

• MLOps Zoomcamp

  • Module 6: Best Practices
    • Homework
  • Module 07: Course Project
    • Course Project ## Related concepts

• Docker

• Terraform

• Model Deployment

• Playwright

• OpenTelemetry