AI Model Parameters Guide

ot; Deployment options:") for target in analysis['suitable_targets']: print(f" - {target['target']}: " f"{target['memory_usage_percent']:.1f}% memory, " f"~{target['estimated_inference_ms']:.0f}ms inference")

deployment_guide = DeploymentConsiderations()
deployment_guide.create_deployment_guide()

## ✅ Best Practices for Parameter Management

### Model Selection Guidelines

When choosing or designing AI models, consider these parameter-related factors:

1. **Task Complexity**: More complex tasks generally require more parameters
2. **Data Availability**: Large parameter models need large datasets
3. **Resource Constraints**: Consider training and deployment limitations
4. **Performance Requirements**: Balance accuracy needs with efficiency
5. **Maintenance Costs**: Larger models are more expensive to maintain

### Optimization Strategies

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