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Deploy your Serverless AI App in 10 Minutes with Code Engine

IntermediateGuided Project

Do you want to deploy a serverless AI model like a software engineer using technologies such as Docker containers and Kubernetes? This guided project will show you how to deploy a text-generation app based on the large GPT2 model in 10 mins. On the one hand, this project does not require knowledge of front-end and back-end development. On the other hand, the model deployment comes at no cost! You get free resources on IBM Cloud to experiment with deploying the AI model you like and share the app.

4.5 (26 Reviews)

Language

  • English

Topic

  • Cloud Computing

Enrollment Count

  • 350

Skills You Will Learn

  • Machine Learning, Code Engine, IBM Cloud, Model Deployment

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 60 minutes

Platform

  • SkillsNetwork

Last Update

  • December 7, 2025
About this Guided Project

A Look at the Project Ahead


In this project, we will show you step-by-step how to use IBM Code Engine to deploy your AI application on IBM Cloud. IBM Code Engine is a fully managed, serverless platform which provides an abstraction for the underlying infrastructure required to deploy your apps and lets you focus on the source code only (such as the Python code). The following picture shows an example of the text-generation model. On the left-hand side, the user provides a text prompt, which the GPT2 model will take as an input and return an output on the right-hand side. You can also access and play with the application here.



You will also use Gradio, a Python framework that allows you to build demos or interactive apps of your Machine Learning models, APIs, or Data Science workflows and share them. Gradio lets you quickly generate a user interface for your application with just a few lines of code, which means even without knowledge of HTML or CSS, you can still create a simple UI by calling its powerful Python APIs.

Learning Objectives:

  • Know how to wrap a Machine Learning model inside Gradio’s interface.
  • Understand containerization.
  • Have hands-on experience with containerization.
  • Become familiar with IBM Code Engine.
  • Know how to use Code Engine to create and store container images on IBM Cloud.
  • Deploy the Machine Learning app from the container image.
  • Learn good practices and troubleshooting with IBM Code Engine.


What You'll Need


There is no prerequisite for this project. We will teach you all the Python code and the Code Engine CLI commands. We will make sure you understand everything as you go through the steps of the project. If you don’t have a finished AI model, don’t worry. We will show you how to import a GPT2 model pipeline from HuggingFace. We will also teach you some basics of deploying containerized applications.

Instructors

Roxanne Li

Data Scientist at IBM

I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!

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Contributors

Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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