Model Lifecycle Management

Comprehensive management of LLM models from selection to retirement, ensuring optimal performance and cost efficiency throughout their lifecycle.

Overview

Model Lifecycle Management in LLMOps involves managing the entire journey of LLM models from initial selection through production deployment, monitoring, updates, and eventual retirement. This process ensures models remain effective, cost-efficient, and aligned with business objectives.

Lifecycle Stages

1. Model Selection & Evaluation

Choose the right model for your specific use case:

// Evaluate models for your use case
const evaluation = await ants.llmops.evaluateModels({
  useCase: 'customer-support',
  requirements: {
    maxLatency: 2000, // 2 seconds
    maxCostPerQuery: 0.01,
    languages: ['en', 'es', 'fr'],
    capabilities: ['reasoning', 'code-generation']
  },
  candidates: ['gpt-4', 'claude-3', 'llama-2-70b']
})
 
console.log('Best model:', evaluation.recommended)
console.log('Performance scores:', evaluation.scores)
console.log('Cost analysis:', evaluation.costAnalysis)

2. Model Registration & Versioning

# Register a new model version
model_registry = ants.llmops.model_registry
 
model = model_registry.register({
    'name': 'customer-support-v2',
    'provider': 'openai',
    'model_id': 'gpt-4-turbo',
    'version': '2.1.0',
    'metadata': {
        'description': 'Customer support model with improved empathy',
        'training_data': '2024-Q3-support-tickets',
        'performance_targets': {
            'accuracy': 0.95,
            'latency_p95': 1500,
            'cost_per_query': 0.008
        }
    }
})
 
print(f"Model registered: {model.id}")

3. Model Testing & Validation

Comprehensive testing before deployment:

// Automated model testing
const testSuite = await ants.llmops.createTestSuite({
  modelId: 'customer-support-v2',
  tests: [
    {
      name: 'accuracy-test',
      dataset: 'support-tickets-test-set',
      metrics: ['accuracy', 'f1-score', 'response-quality']
    },
    {
      name: 'latency-test',
      loadProfile: 'production-traffic',
      thresholds: { p95: 2000, p99: 3000 }
    },
    {
      name: 'cost-test',
      scenarios: ['simple-queries', 'complex-queries'],
      maxCostPerQuery: 0.01
    }
  ]
})
 
const results = await testSuite.run()
console.log('Test results:', results.summary)

4. Model Deployment

Deploy models with proper monitoring:

# Deploy model to production
deployment = ants.llmops.deploy({
    'model_id': 'customer-support-v2',
    'environment': 'production',
    'traffic_percentage': 10,  # Start with 10% traffic
    'monitoring': {
        'metrics': ['latency', 'cost', 'accuracy', 'error_rate'],
        'alerts': [
            {'metric': 'latency_p95', 'threshold': 2000},
            {'metric': 'error_rate', 'threshold': 0.05},
            {'metric': 'cost_per_query', 'threshold': 0.012}
        ]
    },
    'rollback_strategy': 'automatic'
})
 
print(f"Deployment ID: {deployment.id}")
print(f"Status: {deployment.status}")

5. Model Monitoring & Performance Tracking

Continuous monitoring of model performance:

// Monitor model performance
const monitoring = await ants.llmops.getModelMetrics({
  modelId: 'customer-support-v2',
  timeRange: 'last_7_days',
  metrics: [
    'latency',
    'throughput',
    'error_rate',
    'cost_per_query',
    'user_satisfaction',
    'accuracy'
  ]
})
 
console.log('Performance Summary:')
console.log(`Average Latency: ${monitoring.latency.avg}ms`)
console.log(`P95 Latency: ${monitoring.latency.p95}ms`)
console.log(`Error Rate: ${monitoring.errorRate}%`)
console.log(`Cost per Query: $${monitoring.costPerQuery}`)
console.log(`User Satisfaction: ${monitoring.userSatisfaction}%`)

6. Model Updates & Retraining

Manage model updates and retraining cycles:

# Schedule model retraining
retraining_job = ants.llmops.schedule_retraining({
    'model_id': 'customer-support-v2',
    'trigger': 'performance_degradation',
    'thresholds': {
        'accuracy_drop': 0.05,
        'latency_increase': 0.2
    },
    'retraining_config': {
        'data_source': 'latest-support-tickets',
        'validation_split': 0.2,
        'hyperparameters': {
            'learning_rate': 0.001,
            'batch_size': 32
        }
    }
})
 
print(f"Retraining scheduled: {retraining_job.id}")

7. Model Retirement

Properly retire outdated models:

// Retire model with data preservation
const retirement = await ants.llmops.retireModel({
  modelId: 'customer-support-v1',
  reason: 'replaced-by-v2',
  dataRetention: {
    metrics: '1_year',
    logs: '6_months',
    artifacts: 'archive'
  },
  migration: {
    targetModel: 'customer-support-v2',
    dataMigration: true
  }
})
 
console.log(`Model retired: ${retirement.id}`)
console.log(`Data archived: ${retirement.archiveLocation}`)

Model Registry Features

Centralized Model Inventory

// Browse model registry
const registry = await ants.llmops.getModelRegistry({
  filters: {
    status: 'production',
    provider: 'openai',
    tags: ['customer-support', 'v2']
  }
})
 
registry.models.forEach(model => {
  console.log(`${model.name} (${model.version})`)
  console.log(`Status: ${model.status}`)
  console.log(`Performance: ${model.performance.score}`)
  console.log(`Cost: $${model.costPerQuery}/query`)
})

Model Lineage Tracking

# Track model lineage
lineage = ants.llmops.get_model_lineage('customer-support-v2')
 
print("Model Lineage:")
for version in lineage.versions:
    print(f"  {version.version}: {version.description}")
    print(f"    Changes: {version.changes}")
    print(f"    Performance: {version.performance}")

A/B Testing & Experimentation

Model Comparison

// Run A/B test between models
const abTest = await ants.llmops.createABTest({
  name: 'customer-support-model-comparison',
  variants: [
    {
      name: 'control',
      modelId: 'customer-support-v1',
      trafficPercentage: 50
    },
    {
      name: 'treatment',
      modelId: 'customer-support-v2',
      trafficPercentage: 50
    }
  ],
  metrics: ['accuracy', 'latency', 'cost', 'user_satisfaction'],
  duration: '2_weeks',
  statisticalSignificance: 0.95
})
 
const results = await abTest.getResults()
console.log('A/B Test Results:', results.summary)

Gradual Rollout

# Gradual model rollout
rollout = ants.llmops.gradual_rollout({
    'model_id': 'customer-support-v2',
    'strategy': 'canary',
    'stages': [
        {'percentage': 5, 'duration': '1_day'},
        {'percentage': 25, 'duration': '3_days'},
        {'percentage': 50, 'duration': '1_week'},
        {'percentage': 100, 'duration': 'indefinite'}
    ],
    'rollback_triggers': [
        {'metric': 'error_rate', 'threshold': 0.05},
        {'metric': 'latency_p95', 'threshold': 2000}
    ]
})
 
print(f"Rollout started: {rollout.id}")

Best Practices

1. Model Selection

Evaluate multiple models for each use case
Consider total cost of ownership (not just per-query costs)
Test with real data before committing to a model
Plan for model updates and versioning

2. Version Control

Use semantic versioning for model versions
Maintain detailed change logs for each version
Tag models with metadata (use case, performance, etc.)
Keep previous versions available for rollback

3. Testing Strategy

Automated testing for every model update
Performance regression testing to catch degradations
Cost impact analysis for budget planning
User acceptance testing for quality assurance

4. Monitoring & Alerting

Set up comprehensive monitoring from day one
Define clear SLAs for model performance
Implement automated alerting for anomalies
Regular performance reviews and optimization

5. Lifecycle Management

Plan model retirement from the beginning
Maintain data retention policies for compliance
Document all decisions and rationale
Regular lifecycle reviews and updates

Integration with FinOps, SRE, and Security Posture

FinOps Integration

Cost tracking per model and version
Budget allocation and alerts
ROI analysis for model investments
Cost optimization recommendations

SRE Integration

Performance monitoring and SLAs
Incident response for model failures
Capacity planning for model scaling
Reliability metrics and reporting

Security Posture Integration

Model security scanning and validation
Access control for model registry
Audit trails for model changes
Compliance reporting for model usage

Next: Prompt Operations →

Overview Prompt Operations