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CI/CD, Testing and Safe Releases

Build a pipeline that checks every change, builds one image, and promotes it to staging and production with an approval. Then release an AI model change safely, migrate a database without downtime, roll back and ship a hotfix.

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What you'll learn

By the end of this course, you will be able to:

  • Build a repeatable validation and deployment pipeline.
  • Separate development, staging and production.
  • Release a tested artifact with traceable approvals.
  • Handle migrations, hotfixes and rollback.

About this course

Continuous integration and continuous delivery (CI/CD) is the path that every change takes from your computer to your users. A machine checks the change, builds it once, and moves the same result from one environment to the next. No person copies files by hand, and no unchecked change reaches production.

In this course, you build that path for the ticket API from the earlier courses, with GitHub Actions. Each change runs lint, tests, a security check and an AI evaluation gate. The pipeline builds one container image, tests it, and promotes that exact image to a staging environment and, after an approval, to production on Azure Container Apps.

Then you release the changes that make AI systems risky. A new classifier version is better on average, but the gate catches a change that breaks the most important tickets. A database migration must keep the old version working while the new one starts. A canary shows the new model to one request in ten before everyone gets it. When a real bug escapes, you roll back, ship a hotfix from the release tag, and make sure the fix is not lost.

Everything in the lessons happened for real, in a public repository that you can open: the pull requests, the failed and passed checks, the approvals, the releases and the rollback. Each lesson also shows a common mistake: what you see, why it happens and how to recover.

Before you start

You completed Git and Team Development Essentials, Build and Deploy Your First API, and Docker and Cloud Essentials for AI Developers, or you can already work with branches and pull requests, test a FastAPI service, and build and run its container image. You need a free GitHub account. An Azure account is optional; every cloud step has a local simulation.

Courses to take first: Git and Team Development Essentials, Build and Deploy Your First API, Docker and Cloud Essentials for AI Developers.

Who this is for

People who can containerize and deploy a small API, as in Docker and Cloud Essentials, and want every change to reach users through the same safe, repeatable path. Teams expect this from anyone who ships AI features.

Lead or use AI, but do not build it?

You can study the ideas of this course without writing code. Read modules 1 and 5 (about 2–3 hours), answer their knowledge checks, and get a free concepts certificate. You can come back for the practice later.

The paths for people who lead or use AI take this course this way. See the paths

How we made this course

Experienced AI practitioners designed this course. AI helped us draft the content; people chose the teaching approach and the examples, and reviewed every lesson. The outputs that you see are the actual results of running the code. How we make our courses

Inside the course

Module 1ConceptsUnderstand the delivery path

3 lessons

Follow a change from a commit to a release. You learn triggers, jobs, runners, artifacts and environments, choose a branch workflow, and decide which checks protect production.

  1. From commit to release40 min
  2. Choose a branch workflow45 min
  3. Define checks50 min

Module 2Create a repeatable CI pipeline

3 lessons

Build a GitHub Actions pipeline that runs lint, tests and a security check on a clean runner, builds one versioned image, smoke-tests it and publishes it with evidence that traces it to its commit.

  1. Runner setup45 min
  2. Build and test45 min
  3. Publish evidence50 min

Module 3Manage environments and secrets

3 lessons

Run the same image in staging and production with different settings. Each environment signs in to the cloud with its own identity and no stored password, and production waits for an approval.

  1. Configuration separation40 min
  2. Credentials and access45 min
  3. Promote to staging50 min

Module 4Release AI and database changes

3 lessons

Release AI and database changes safely. An evaluation gate blocks a worse model, an expand-and-contract migration keeps old and new versions working together, and a canary sends a small share of traffic to the new model first.

  1. AI evaluation gate45 min
  2. Migration safety50 min
  3. Progressive release50 min

Module 5ConceptsRecover and maintain the release process

3 lessons

Recover when a release goes wrong. Choose between rolling back the application, the model or the data, ship a hotfix from the release tag, merge it back, and write release notes that someone else can act on.

  1. Rollback strategy45 min
  2. Hotfix procedure50 min
  3. Release communication50 min

After Module 5Final assignment

Use every module in one practice project. You collect evidence, and check it against the course outcomes.

  1. Final assignment180 min

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