Listing Available Models¶

Learn how to discover, explore, and select models in Azure AI Foundry for your specific use cases.

Model Discovery¶

1. Model Categories¶

• Text Generation
• Code Generation
• Image Generation
• Speech Processing
• Custom Models

```python from azure.identity import DefaultAzureCredential from azure.ai.resources import AIProjectClient from typing import Dict, List

class ModelExplorer: def init(self): """Initialize the ModelExplorer with Azure credentials.""" try: self.credential = DefaultAzureCredential() self.client = AIProjectClient( subscription_id=os.getenv("AZURE_SUBSCRIPTION_ID"), resource_group=os.getenv("AZURE_RESOURCE_GROUP"), credential=self.credential ) except Exception as e: print(f"Error initializing ModelExplorer: {str(e)}") raise

def list_models_by_category(self) -> Dict[str, List[dict]]:
    """List all available models grouped by category."""
    try:
        # Get all available models
        models = self.client.models.list()

        # Group models by category
        categorized_models = {}
        for model in models:
            category = model.category
            if category not in categorized_models:
                categorized_models[category] = []

            model_info = {
                'name': model.name,
                'version': model.version,
                'description': model.description,
                'capabilities': model.capabilities
            }
            categorized_models[category].append(model_info)

        return categorized_models
    except Exception as e:
        print(f"Error listing models: {str(e)}")
        raise

def display_models(self):
    """Display available models in a formatted way."""
    try:
        models = self.list_models_by_category()

        for category, model_list in models.items():
            print(f"\n=== {category} Models ===")
            for model in model_list:
                print(f"\nName: {model['name']}")
                print(f"Version: {model['version']}")
                print(f"Description: {model['description']}")
                print(f"Capabilities: {', '.join(model['capabilities'])}")
    except Exception as e:
        print(f"Error displaying models: {str(e)}")

Example usage¶

if name == "main": explorer = ModelExplorer() explorer.display_models()

2. Model Providers¶

• Azure OpenAI
• Third-party providers
• Custom providers
• Community models

3. Model Capabilities¶

• Task specialization
• Language support
• Performance characteristics
• Resource requirements

Model Selection¶

1. Selection Criteria¶

• Use case requirements
• Performance needs
• Resource constraints
• Cost considerations

2. Comparison Factors¶

• Model capabilities
• Version differences
• Resource usage
• Pricing tiers

3. Evaluation Methods¶

• Performance metrics
• Quality assessment
• Resource efficiency
• Cost analysis

Model Information¶

1. Technical Details¶

• Model architecture
• Version history
• Input/output formats
• Resource specifications

2. Usage Guidelines¶

• Best practices
• Limitations
• Performance tips
• Security considerations

3. Deployment Requirements¶

• Resource needs
• Scaling considerations
• Integration requirements
• Monitoring setup

Best Practices¶

1. Model Selection¶

• Define clear requirements
• Compare alternatives
• Test performance
• Consider costs

2. Resource Planning¶

• Capacity planning
• Usage monitoring
• Cost optimization
• Performance tuning

3. Documentation¶

• Track decisions
• Document configurations
• Monitor changes
• Share knowledge

Interactive Workshop¶

For hands-on practice with exploring available models in Azure AI Foundry, try our interactive notebook:

Launch Available Models Workshop

This notebook provides: - Comprehensive model listing and filtering - Detailed model information retrieval - Version comparison capabilities - Best practices for model selection - Interactive examples with real-time output

Next: Deploying Models