AI Model Management Guide¶

Comprehensive guide for managing, deploying, and monitoring AI models in your Local AI Cyber Lab environment.

Overview¶

Model management in Local AI Cyber Lab provides a centralized system for handling the complete lifecycle of AI models, from deployment to monitoring and version control.

Model Management Features¶

Model Registry¶

Version control for models
Model metadata tracking
Dependency management
Model lineage tracking

Deployment Management¶

Zero-downtime deployments
A/B testing support
Rollback capabilities
Resource allocation

Model Serving¶

REST API endpoints
Batch inference
Real-time predictions
Load balancing

Working with Models¶

Adding New Models¶

# Pull a model from Ollama
ollama pull mistral
ollama pull codellama

# List available models
ollama list

# Check model status
ollama status mistral

Model Configuration¶

model:
  name: mistral
  version: 1.0
  parameters:
    temperature: 0.7
    max_tokens: 2048
    top_p: 0.9
  resources:
    gpu_memory: 8G
    cpu_limit: 4

Deployment Examples¶

Basic Deployment¶

# Deploy a model
ollama run mistral

# Deploy with custom configuration
ollama run mistral --config config.yaml

Advanced Deployment¶

from ollama import Ollama

# Initialize client
client = Ollama()

# Deploy model with specific configuration
model = client.deploy(
    "mistral",
    config={
        "temperature": 0.7,
        "max_tokens": 2048
    }
)

Model Monitoring¶

Performance Metrics¶

Inference latency
Throughput
Error rates
Resource utilization

Usage Analytics¶

Request patterns
Token usage
User statistics
Cost analysis

Best Practices¶

Resource Management¶

Monitor GPU utilization
Implement caching strategies
Optimize batch sizes
Use appropriate quantization

Version Control¶

Use semantic versioning
Document model changes
Track dependencies
Maintain model cards

Security¶

Implement access controls
Monitor for anomalies
Regular security scans
Data validation

Integration Examples¶

API Integration¶

import requests

def query_model(prompt):
    response = requests.post(
        "http://localhost:11434/api/generate",
        json={
            "model": "mistral",
            "prompt": prompt
        }
    )
    return response.json()

Batch Processing¶

def batch_process(inputs, model_name="mistral"):
    results = []
    for input_data in inputs:
        result = query_model(input_data)
        results.append(result)
    return results

Troubleshooting¶

Common Issues¶

Memory allocation errors
GPU compatibility issues
API timeout errors
Version conflicts

Solutions¶

Check resource allocation
Verify GPU drivers
Adjust timeout settings
Update dependencies

Support¶

For model management assistance: - 📧 Email: support@cyber-ai-agents.com - 📚 Documentation - 💬 Community Forum