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1. AzureVision: A Custom Computer Vision Solution

Skills covered: Implement computer vision solutions, Plan and manage an Azure AI solution

Project Description: Build a custom image classification and object detection system using Azure AI Vision. This project will help you create, train, and deploy custom computer vision models for specific business scenarios.

Azure Services Used:

  • Azure AI Vision
  • Azure Key Vault
  • Azure Monitor

Steps:

  1. Create Azure AI resources with appropriate authentication
  2. Upload and label a dataset of images for a specific domain
  3. Train custom image classification and object detection models
  4. Evaluate model metrics and fine-tune performance
  5. Deploy models to production endpoints
  6. Build a Python application that consumes the models
  7. Set up monitoring and logging
  8. Implement security best practices for API keys

2. MultiLingual Assistant: A Natural Language Processing Hub

Skills covered: Implement natural language processing solutions, Create a custom question answering solution

Project Description: Develop a comprehensive NLP system that combines text analysis, speech processing, translation, and question answering capabilities.

Azure Services Used:

  • Azure AI Language
  • Azure AI Speech
  • Azure AI Translator
  • Azure Key Vault

Steps:

  1. Set up Language service to extract entities and sentiment from text
  2. Implement speech-to-text and text-to-speech functionality
  3. Create a custom question answering solution with multi-turn conversations
  4. Train a language understanding model with intents and entities
  5. Implement translation capabilities for multiple languages
  6. Build a Python client application that integrates all services
  7. Optimize models based on evaluation metrics
  8. Set up proper authentication and security

3. DocumentMiner: An Intelligent Document Processing System

Skills covered: Implement knowledge mining and document intelligence solutions

Project Description: Create a solution that automatically extracts, indexes, and makes searchable information from various document types.

Azure Services Used:

  • Azure AI Document Intelligence
  • Azure AI Search
  • Azure Blob Storage
  • Azure Functions

Steps:

  1. Provision Azure AI Document Intelligence and Azure AI Search resources
  2. Use prebuilt models to extract data from common document types
  3. Create a custom document intelligence model for specialized documents
  4. Set up Azure AI Search index with custom skillsets
  5. Implement a document processing pipeline
  6. Build a search interface in Python to query the indexed documents
  7. Create Knowledge Store projections for document insights
  8. Set up monitoring and continuous improvement

4. ContentSafe: A Content Moderation Platform

Skills covered: Implement content moderation solutions, Plan and manage an Azure AI solution

Project Description: Build a system that automatically detects and filters inappropriate content in text and images.

Azure Services Used:

  • Azure AI Content Safety
  • Azure Functions
  • Azure Blob Storage
  • Azure Monitor

Steps:

  1. Set up Azure AI Content Safety resources
  2. Implement text moderation for detecting offensive language
  3. Implement image moderation for inappropriate visual content
  4. Create serverless functions to process content in real-time
  5. Set up storage for flagged content review
  6. Build a Python dashboard for manual review of borderline cases
  7. Configure monitoring and alerting
  8. Implement responsible AI principles

5. CreativeGenius: A Generative AI Solution

Skills covered: Implement generative AI solutions, Plan and manage an Azure AI solution

Project Description: Create an application that leverages Azure OpenAI Service for generating content, answering questions, and creating images.

Azure Services Used:

  • Azure OpenAI Service
  • Azure Key Vault
  • Azure Functions
  • Azure Blob Storage

Steps:

  1. Provision an Azure OpenAI Service resource
  2. Deploy appropriate models (text generation, code generation, DALL-E)
  3. Implement prompt engineering techniques to optimize responses
  4. Create a Python application that interfaces with the OpenAI APIs
  5. Implement a system to use custom data with Azure OpenAI models
  6. Build a secure endpoint for generating images with DALL-E
  7. Set up authentication and API key management
  8. Implement monitoring and usage tracking