Azure AI & Data Analytics Hackathon: Retail Intelligence Solutions

This intensive 2-3 day guided hackathon provides hands-on experience building end-to-end AI and data analytics solutions for retail business challenges. Participants will work with Woodgrove Retail's dataset to develop comprehensive solutions using Azure's AI and data services, progressing from basic SQL analysis to advanced machine learning and retrieval-augmented generation (RAG) implementations.

Target Audience

• Data analysts and business intelligence professionals
• Software developers interested in AI/ML applications
• Solutions architects working with Azure services
• Technical professionals seeking practical experience with modern AI workflows

Prerequisites

• Basic SQL knowledge and database concepts
• Familiarity with Python programming
• Understanding of web development fundamentals (HTML, JavaScript)
• Azure account with appropriate permissions
• Basic machine learning concepts (helpful but not required)

Learning Objectives

By the end of this hackathon, participants will be able to:

1. Analyze retail data using SQL Server to identify business patterns and challenges
2. Build and deploy machine learning models using Azure ML Designer for predictive analytics
3. Implement computer vision solutions using Azure Vision Studio for product image recognition
4. Create intelligent search experiences using Azure Cognitive Search and the RAG pattern
5. Develop integrated applications combining multiple AI services into unified solutions
6. Deploy end-to-end solutions that demonstrate real-world business value

Course Structure & Daily Breakdown

Day 1: Data Foundation & ML Fundamentals

Morning Session (4 hours)

• Challenge 1: SQL Server Data Analysis
  • Connect to Azure SQL Database using SQL Server Management Studio
  • Explore Woodgrove Retail's database schema (Orders, Customers, Suppliers, Articles)
  • Write queries to analyze return rates, supplier reliability, and sales trends
  • Identify data patterns contributing to business challenges

Afternoon Session (4 hours)

• Challenge 2: Machine Learning Classification
  • Set up Azure Machine Learning workspace and compute resources
  • Build classification models using Azure ML Designer
  • Implement data preparation and feature engineering
  • Train models to predict high-risk return orders
  • Deploy prediction endpoints and test with sample data

Day 2: Computer Vision & Intelligent Search

Morning Session (4 hours)

• Challenge 3: Image-Based Product Search
  • Configure Azure Vision Studio for custom model training
  • Process and label product image datasets across 9 categories
  • Train custom computer vision models for product recognition
  • Implement image-based search functionality
  • Test model accuracy with validation images

Afternoon Session (4 hours)

• Challenge 4: RAG Pattern Implementation
  • Convert SQL data to PDF documents with embedded images
  • Set up Azure Cognitive Search with AI enrichment
  • Extract key attributes (Article Number, Category, Content, Color, Location)
  • Build semantic search capabilities
  • Demonstrate enhanced search experiences

Day 3: Advanced Integration & Deployment

Morning Session (4 hours)

• Challenge 5: Advanced RAG with Multi-Service Integration
  • Export and process data using advanced SQL queries
  • Create vector embeddings and search indexes in Azure AI Studio
  • Generate inventory recommendations using AI prompts
  • Import generated data into Azure Cosmos DB
  • Build web applications with Node.js, Express, and Bootstrap

Afternoon Session (3-4 hours)

• Challenge 6: Solution Integration & Presentation
  • Integrate all components into unified solution architecture
  • Implement incremental RAG pattern updates
  • Final testing and optimization
  • Team presentations and solution demonstrations
  • Best practices discussion and next steps

Key Technologies & Azure Services

• Data & Analytics: Azure SQL Database, SQL Server Management Studio
• Machine Learning: Azure Machine Learning, ML Designer, Jupyter Notebooks
• Computer Vision: Azure Vision Studio, Custom Vision models
• Search & AI: Azure Cognitive Search, Azure OpenAI, Azure AI Studio
• Storage & Databases: Azure Blob Storage, Azure Cosmos DB
• Development: Node.js, Express.js, Bootstrap, Python, JavaScript

Business Scenario Context

Participants work with Woodgrove Retail, a German-based online retailer facing critical business challenges:

• Significant decline in sales and profits
• High return rates due to defective items and customer behavior
• Need for data-driven insights to improve operations
• Requirements for enhanced customer experience through AI

Deliverables & Success Criteria

Each challenge includes specific success criteria and deliverables:

1. Working SQL queries demonstrating data analysis insights
2. Deployed ML models with accuracy metrics above baseline
3. Functional image recognition system with product matching
4. Enhanced search experience using cognitive skills and RAG
5. Integrated web application displaying multi-source data
6. Comprehensive solution demonstrating end-to-end AI workflow

Assessment & Certification

• Hands-on challenge completion with coach validation
• Technical demonstrations of working solutions
• Team presentations showcasing business impact
• Optional: Azure certification pathway recommendations

Additional Resources

• Microsoft Learn modules for each Azure service
• Sample code repositories and Jupyter notebooks
• Architecture guidance and best practices documentation
• Post-hackathon learning paths and community resources

This hackathon provides practical, real-world experience building AI solutions that address genuine business challenges, preparing participants to implement similar solutions in their own organizations.