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Created by James Quigley
Design a practical promotion model that maps branches, PRs, and immutable tags to test → staging → production. Then implement the gating mechanics in GitHub Actions using tests, GitHub Environments, scoped configuration, deployment approvals, and concurrency controls, with a rollback-ready release strategy.
6 modules • Each builds on the previous one
Model how code moves from test to staging to live using a team branching strategy plus immutable version tags that make each deployment traceable and reproducible. ([docs.github.com](https://docs.github.com/en/actions/how-tos/create-and-publish-actions/using-immutable-releases-and-tags-to-manage-your-actions-releases?utm_source=openai))
Learn how GitHub Actions triggers, jobs, runners, artifacts, and contexts fit together so automated tests produce evidence that gates merges and deployments. ([docs.github.com](https://docs.github.com/actions/using-workflows/storing-workflow-data-as-artifacts?utm_source=openai))
Define test, staging, and production as GitHub Environments and understand how a deployment job targets an environment to create deployments, attach URLs, and enable environment-specific controls. ([docs.github.com](https://docs.github.com/en/enterprise-cloud%40latest/actions/reference/workflows-and-actions/deployments-and-environments?utm_source=openai))
Design least-privilege configuration by scoping secrets and variables to each environment, understanding inheritance behavior, and avoiding common failure modes with reusable workflows and dynamic environment selection. ([docs.github.com](https://docs.github.com/en/actions/configuring-and-managing-workflows/creating-and-storing-encrypted-secrets?utm_source=openai))
Use environment deployment protection rules—required reviewers, wait timers, branch/tag restrictions, and custom GitHub App gates—to prevent accidental or unauthorized production deploys, and understand how GitHub can block self-approvals when configured. ([docs.github.com](https://docs.github.com/en/enterprise-server%403.15/actions/reference/deployments-and-environments?utm_source=openai))
Control parallelism with concurrency groups and deployment queues so only the right deployment runs for an environment, then pair it with a rollback playbook that redeploys a known-good tag or release when regressions appear. ([docs.github.com](https://docs.github.com/en/actions/using-jobs/using-concurrency?utm_source=openai))
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