Azure AI Foundry is a platform designed for AI development on Microsoft Azure. It helps developers build AI applications efficiently by providing tools for resource management, collaboration, and AI model development.
Key Features:
- Hubs & Projects: Organize AI resources, assets, and code. Hubs manage shared resources, while projects allow teams to collaborate on specific AI solutions.
- Model Catalog: Access and deploy machine learning models from sources like Azure OpenAI and Hugging Face.
- Playgrounds: Test prompts with generative AI models.
- Fine-Tuning: Customize AI models using training prompts.
- Prompt Flow: Define logic for AI interactions.
- Security & Access Control: Manage user roles and permissions centrally.
Here are some examples of projects built using Azure AI Foundry:
1. AI-Powered Flight Booking Agent
- A conversational AI agent that helps users find flights based on their preferences.
- Uses Azure AI Agent service to interact with users and provide flight details.
- Demonstrates the potential of Azure’s conversational AI capabilities.
2. Generative AI Model Deployment
- Developers can deploy models like GPT-4o-mini for AI applications.
- Fine-tuning and prompt orchestration using Prompt Flow.
- Helps businesses customize AI models for specific use cases.
3. Embedded AI Samples for Developers
- A collection of open-source AI projects available on GitHub.
- Includes notebooks and sample code for AI Foundry scenarios.
- Developers can experiment with AI models and integrate them into applications.
Here’s how you can start your Azure AI Foundry project:
1. Define Your AI Goal
- Identify what you want to achieve with AI (e.g., chatbots, predictive analytics, automation).
- Choose whether you need pre-trained models or want to fine-tune your own.
2. Set Up Your AI Foundry Environment
- Access Azure AI Foundry via the Azure portal.
- Create a Hub to manage AI resources and a Project to collaborate with teams.
3. Explore AI Models
- Use the Model Catalog to access OpenAI models, Hugging Face models, and other AI solutions.
- Experiment with different models in the Playground.
4. Fine-Tune Your Model
- Train models using Prompt Flow to customize responses.
- Use Fine-Tuning to make your AI more aligned with your specific use case.
5. Deploy and Test
- Deploy your AI solution to cloud services or integrate with applications.
- Test responses and improve logic with AI Foundry’s orchestration tools.