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Docker and Cloud Essentials for AI Developers

Package the ticket API in a container image, run it with a database on your computer, and deploy it to Azure Container Apps with its settings outside the image, a cost check and a full clean-up.

  • 15 lessons
  • 7–10 hr
  • Beginner to intermediate
  • Check in every lesson
  • Free certificate
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Read every lesson as a guest, or sign in to enroll and track your progress.

What you'll learn

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

  • Explain images, containers, ports and volumes.
  • Containerize and run a small API.
  • Map application needs to cloud building blocks.
  • Deploy with cost controls and a cleanup procedure.

About this course

A container is a package that holds an application with everything it needs to run: the Python version, the packages and the operating system files. The same package runs the same way on your laptop, on a colleague’s computer and on a cloud server. This is why most AI services, from small APIs to large model servers, run in containers.

In this course, you put the ticket API from Build and Deploy Your First API in a container. You build the image step by step and see why each line of the Dockerfile is there. Then you run the API with a PostgreSQL database on your computer, with Docker Compose, and prove which data survives when a container is replaced.

Then you take the same image to the cloud. You learn the building blocks that every cloud has, with Azure as the example: a registry for images, a service that runs containers, an identity instead of a password, and the charges that each part can make. You deploy the API to Azure Container Apps and check it. Then you delete everything, and you prove that nothing is left that can cost money.

Every command in the lessons was run for real, and the lessons show its real output. Each lesson also shows a common mistake: what you see, why it happens and how to recover.

Before you start

You completed Python for Practical AI Engineering and Build and Deploy Your First API, or you can already set up a virtual environment, run a FastAPI service with settings from environment variables, and test it with pytest and curl. You do not need to know Docker or a cloud platform. An Azure account is optional; every cloud step has a local simulation.

Courses to take first: Python for Practical AI Engineering, Build and Deploy Your First API.

Who this is for

People who built a small web API, as in Build and Deploy Your First API, and want it to run the same way on every computer and in the cloud. Most AI services run in containers, so this skill is needed for nearly every deployment.

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 2Build the application image

3 lessons

Write a Dockerfile for the ticket API and build its image. You make builds fast with the layer cache and a .dockerignore file, pin every version, run as a user that is not root, and keep secrets out of the image.

  1. Dockerfile essentials50 min
  2. Build efficiently50 min
  3. Runtime safety50 min

Module 3Networking, configuration and persistence

3 lessons

Connect to the container through a port, give it its settings and secrets when it starts, and keep data that must survive. You run the API with a PostgreSQL database with Docker Compose.

  1. Ports and service access40 min
  2. Runtime configuration45 min
  3. Persistent data50 min

After Module 5Final assignment

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

  1. Final assignment120 min

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