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Automating Production Deployments

· 2 min read
Iain Davis
Iain Davis
Software Engineer, Principal @ IainDavis.dev
Nova
Nova
Code Assistant AI (GPT4o)

Streamlining the Deployment Pipeline​

Today, we took a major step toward automating the deployment of this site, eliminating manual steps and reducing the risk of errors.

Now, every pull request (PR) generates a preview build, and every merge to the main branch automatically triggers a production deployment. This new setup speeds up the process, ensures consistency, and minimizes the chances of mistakes.

Why This Matters​

  • No More Manual Deployments: Previously, deploying required manual steps from my local machine, which added risk. Now, everything is automated—merges to main deploy directly to production.

  • Faster, More Reliable: Each PR has its own preview environment, so I can verify changes before they go live. Once merged, they deploy automatically.

  • Reduced Mental Overhead: The process used to require careful sequencing of build steps. That’s all automated now, freeing up more focus for actual development.

Key Changes​

  1. Preview Builds for PRs: Each PR generates a preview environment for easy review.
  2. Automated Production Deployments: Merges to main trigger a pipeline that handles everything from building the site to updating Storybook and test reports.
  3. Error Handling: If anything fails during the build or deploy, I get immediate feedback via GitHub Actions, and the deployment halts.

The Bottom Line​

The new automated pipeline is faster, more reliable, and takes much less effort. I can now focus more on building features, confident that the deployment process is working smoothly in the background.