The #30DaysOf series of repositories were originally intended to provide a structured content-driven roadmap to learn a new topic in a month.
In reality, we all learn at our own pace and no topic can be really mastered in just 30 days. Instead, I view my learning journey as week-long sprints, where I focus on one core concept or tool each week. A #30DaysOf roadmap can be defined from any four sprints that teach a specific topic end-to-end.
Note
Generative AI is a broad topic with many pre-existing resources. Exploring it requires a dual mindset:
- Build It Up - work through pre-existing guides
- Tear It Down - deconstruct open-source projects
The first step teaches core concepts and building blocks from traditional tutorials and documentation sources. The second step helps you skill up on advanced topics and best practices by reverse-engineering existing real-world projects.
My learning journey has three elements:
- Code in Python3 - use Jupyter Notebooks for code & context.
- Protype with Hugging Face - use open-source models & tools.
- Productize with Microsoft Azure - use LLMOps tools & workflows.
To work through exercises, we need:
- A valid GitHub account (with GitHub Codespaces quota)
- Basic familiarity with Python and Jupyter Notebooks
- Basic familiarity with Visual Studio Code - install it for free
- A Hugging Face account and token - create one for free
- An Azure account and subscription - create one for free
- Access to Azure AI services - check requirements here
By taking this learning journey, we should be able to:
- Explain core Generative AI concepts and tools (Platform)
- Understand end-to-end development workflows (LLMOps)
- Prototype open-source generative AI solutions (Hugging Face)
- Productize the generative AI solution ( Azure AI)
- Evaluate quality and ensure safety in solutions (Responsible AI)
- Explore creative and innovative real-world AI uses (Applied AI)
This repository contains both the code and the docs for the journey.
docs/- contains documentation in the form of a Docusarus static site published to GitHub Pages with ability to preview locally.src/contains hands-on labs in the form of Jupyter Notebooks that can be run in GitHub Codespaces or Docker Desktop.
The repository is setup with a .devcontainer that pre-defines all required dependencies for both the documentation and labs requirements. Follow instructions below to get started exploring them with either GitHub Codespaces (cloud) or Docker Desktop (local).
The docs/ folder contains the source for a static Docusaurus site that is published to GitHub Pages. It was created using the default quickstart:
npx create-docusaurus@latest docs classicSee docs/README.md for details on updates to docusaurus.config.js, sidebars.js and other files - for site customization.
To build, preview, or deploy, the site from commandline, first change directory to docs/ then use one of the following commands:
| Command | Description |
|---|---|
npm install |
Install dependencies first |
npm start |
Preview site with dev server |
npm run build |
Build static site for deploy |
npm run serve |
Preview site from static build |
npm run deploy |
Publish static build to GitHub pages |
The .devcontainer folder contains a Dockerfile and devcontainer.json that can setup a prebuilt development environment with all the necessary dependencies for the labs. To get started, launch the devcontainer using one of the two options below:
| Option | Description |
|---|---|
| GitHub Codespaces | Launch a devcontainer in the cloud. Gives you a VM that can be accessed from any device, including from a VS Code instance in the browser. Requires GitHub Codespaces quota - personal free tier is enough. |
| Docker Desktop | Launch a dev container in the local device. Requires pre-installing the free Docker Desktop - your setup may be constrained by device hardware availability but this solution does not require Codespaces quota. |
Once you have a running development container, you should have an active Python 3.x runtime with all necessary dependencies installed. To run an exercise, change directory to src/ and follow these steps:
- Click on the Jupyter notebook for the relevaant lab.
- Click
Select Kernel, pick the recommended Python 3.x option. - Click
Run Allto execute all cells in the notebook. - Alternatively, run cells one-by-one for stepwise walkthrough.
This section will collect FAQ for common issues and workarounds or fixes to resolve them. TODO: Add a custom issue to help readers report problems with specific tags for faster identification and resolution.