Do I need experience with machine learning to take this course?

No. This course focuses on evaluating and integrating pre-built AI models through the GitHub Models service. You will interact with models through APIs and the playground interface rather than building models from scratch. Basic Python knowledge is sufficient.

What tools and accounts do I need?

You need a GitHub account to access GitHub Models and Codespaces. The course uses the free tier of GitHub Models for model evaluation and Codespaces for development. All required Python libraries including OpenAI and Azure AI Inference packages are pre-installed in the Codespaces environment.

Will I build something I can deploy to production?

Yes. In the final module, you build a FastAPI-based HTTP API that connects to GitHub Models, complete with automatic documentation, input validation, and error handling. You will test this endpoint within Codespaces using port forwarding, giving you a production-like environment before deploying to the cloud.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

GitHub: Evaluating and Integrating AI Models

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GitHub: Evaluating and Integrating AI Models

This course is part of Mastering GitHub Specialization

Instructor: Alfredo Deza

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Navigate the GitHub Models marketplace to evaluate and select AI models based on provider capabilities, rate limits, and responsible AI features
Configure GitHub Codespaces development environments and manage scaling from the free tier to Azure AI pay-as-you-go for production workloads
Build, test, and validate HTTP API endpoints using FastAPI that integrate AI models from GitHub Models within Codespaces

Skills you'll gain

Tools you'll learn

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Shareable certificate

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Build your subject-matter expertise

This course is part of the Mastering GitHub Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

Learn to evaluate, select, and integrate AI models using GitHub Models — a service that provides ready-to-use, off-the-shelf machine learning models directly within the GitHub platform. You will navigate the GitHub Models marketplace to compare models by provider, capability, and rate limits, then test them interactively using the built-in playground with system prompts and temperature controls.

This course covers the practical skills needed to move from model evaluation to production integration. You will understand how rate limits work across different models, learn strategies for scaling beyond the free tier through Azure AI integration, and set up cloud development environments using GitHub Codespaces with pre-installed Python libraries. In the final module, you will build a complete HTTP Application Programming Interface (API) using FastAPI that connects to GitHub Models, authenticate using personal access tokens, test your endpoints within Codespaces, and apply validation strategies for production readiness. You will also learn about responsible AI features including content filters that GitHub applies through Azure to ensure safe model interactions. By the end of this course, you will have hands-on experience building and testing AI-powered API endpoints ready for cloud deployment.