Interactive educational exercises and component mockups for teaching AI model fine-tuning concepts
This repository contains an interactive educational exercise that teaches the process of fine-tuning AI models using Azure AI Foundry. The standalone simulation allows learners to understand the complete workflow without requiring actual Azure resources.
Learn fine-tuning concepts through hands-on practice
Works on desktop, tablet, and mobile devices
WCAG 2.1 compliant with screen reader support
Pure HTML, CSS, and JavaScript
By the end of this exercise, learners will be able to:
Interactive component mockups and prototype pages demonstrating various UI patterns for AI model fine-tuning interfaces.
Interactive chat UI for testing prompt/response behavior.
Neutral-themed configuration comparison UI.
Branded comparison view to present model configuration differences.
Components for manual evaluation and scoring of conversation pairs.
UI for selecting fine-tuning options and data subsets.
Simulated workflow to demonstrate fine-tuning stages.
Notebook-like interactive simulation for exercises.
A lightweight, simulated notebook experience for prototyping.
Manual evaluation UI for annotators and reviewers.
Business-focused evaluation scenario and walkthrough.
Ranking UI components for ordering or scoring alternatives.
Gamified terminal-style interaction for guided tasks.
Original Azure DevOps simulation with basic kanban board functionality.
Project-specific Azure DevOps board for Munson's Pickles and Preserves AI meal planner lab.
Fully interactive Azure DevOps board with drag-and-drop, filtering, and realistic project management features.
Interactive Jupyter notebooks with step-by-step examples and exercises.
Jupyter notebook with step-by-step fine-tuning examples (IPYNB).
Interactive notebook exercise for hands-on fine-tuning practice.
Different visual themes and design approaches for the component interfaces.
Clean, minimal design with focus on content and functionality.
Futuristic cyberpunk aesthetic with neon colors and bold typography.
Contemporary card-based design with elegant shadows and spacing.
Game-inspired interface with progress bars and achievement elements.
Gamified quest design with professional white, grey, and blue color scheme.
The main exercise consists of four progressive phases designed to teach fine-tuning concepts step by step.
python -m http.server 8000