Model Versioning & Deployment
Comprehensive versioning and deployment strategies for LLM models, ensuring smooth rollouts, rollbacks, and continuous delivery of AI applications.
Overview
Model Versioning & Deployment in LLMOps provides the framework and tools needed to manage model versions, deploy updates safely, and maintain reliable AI applications in production. This includes version control, deployment pipelines, rollback strategies, and continuous integration for AI models.
Version Control Strategy
Semantic Versioning for Models
typescript
// Implement semantic versioning for models
const versionManager = await ants.llmops.versionManager
// Create new model version
const newVersion = await versionManager.createVersion({
modelId: 'customer-support',
version: '2.1.0', // Major.Minor.Patch
changes: {
type: 'minor', // major, minor, patch
description: 'Improved accuracy for billing queries',
breakingChanges: false,
features: [
'Enhanced billing query classification',
'Improved response quality for refund requests'
],
fixes: [
'Fixed edge case in password reset flow'
]
},
metadata: {
author: 'ml-team',
branch: 'feature/billing-improvements',
commitHash: 'abc123def456',
testResults: {
accuracy: 0.94,
latency: 1800,
costPerQuery: 0.008
}
}
})
console.log(`New version created: ${newVersion.version}`)
console.log(`Version ID: ${newVersion.id}`)
Version Comparison & Analysis
python
# Compare model versions
version_comparator = ants.llmops.version_comparator
# Compare two versions
comparison = version_comparator.compare({
'base_version': 'customer-support-2.0.0',
'target_version': 'customer-support-2.1.0',
'metrics': [
'accuracy',
'latency',
'cost_per_query',
'token_usage',
'user_satisfaction'
]
})
print("Version Comparison Results:")
print(f"Accuracy: {comparison.accuracy.change:+.3f} ({comparison.accuracy.percent_change:+.1%})")
print(f"Latency: {comparison.latency.change:+.0f}ms ({comparison.latency.percent_change:+.1%})")
print(f"Cost: {comparison.cost.change:+.4f} ({comparison.cost.percent_change:+.1%})")
print(f"Overall improvement: {comparison.overall_score:.1%}")
# Get version history
version_history = version_comparator.get_history({
'model_id': 'customer-support',
'limit': 10
})
print("\nVersion History:")
for version in version_history:
print(f"{version.version}: {version.description}")
print(f" Released: {version.release_date}")
print(f" Performance: {version.performance_score:.1%}")
Deployment Pipelines
CI/CD Pipeline for Models
typescript
// Configure CI/CD pipeline for model deployment
const pipelineManager = await ants.llmops.pipelineManager
const deploymentPipeline = await pipelineManager.createPipeline({
name: 'customer-support-deployment',
stages: [
{
name: 'build',
tasks: [
'model-validation',
'prompt-testing',
'security-scan',
'performance-benchmark'
],
triggers: ['code-push', 'model-update']
},
{
name: 'test',
environments: ['development', 'staging'],
tasks: [
'unit-tests',
'integration-tests',
'performance-tests',
'bias-assessment'
],
approval: 'automatic'
},
{
name: 'deploy',
environments: ['production'],
strategy: 'blue-green',
tasks: [
'health-check',
'smoke-tests',
'traffic-switch',
'monitoring-setup'
],
approval: 'manual'
}
],
rollback: {
enabled: true,
triggers: ['error-rate', 'latency', 'accuracy'],
strategy: 'automatic'
}
})
console.log(`Deployment pipeline created: ${deploymentPipeline.id}`)
Automated Testing Pipeline
python
# Set up automated testing pipeline
testing_pipeline = ants.llmops.testing_pipeline
# Configure test suite
test_suite = testing_pipeline.configure({
'model_id': 'customer-support',
'test_categories': [
{
'name': 'accuracy_tests',
'tests': [
'classification_accuracy',
'response_quality',
'edge_case_handling'
],
'thresholds': {
'accuracy': 0.90,
'f1_score': 0.85
}
},
{
'name': 'performance_tests',
'tests': [
'latency_benchmark',
'throughput_test',
'concurrent_load_test'
],
'thresholds': {
'latency_p95': 2000,
'throughput_min': 100
}
},
{
'name': 'security_tests',
'tests': [
'pii_detection',
'content_filtering',
'injection_attack_test'
],
'thresholds': {
'pii_detection_rate': 0.99,
'false_positive_rate': 0.01
}
}
],
'automation': {
'run_on_push': True,
'run_on_schedule': 'daily',
'notify_on_failure': True
}
})
# Run test suite
test_results = testing_pipeline.run({
'version': 'customer-support-2.1.0',
'environment': 'staging'
})
print("Test Results:")
for category, results in test_results.categories.items():
print(f"\n{category}:")
print(f" Status: {results.status}")
print(f" Score: {results.score:.1%}")
for test in results.tests:
print(f" {test.name}: {test.status} ({test.score:.1%})")
Deployment Strategies
Blue-Green Deployment
typescript
// Implement blue-green deployment strategy
const deploymentManager = await ants.llmops.deploymentManager
const blueGreenDeployment = await deploymentManager.createBlueGreenDeployment({
modelId: 'customer-support',
version: '2.1.0',
strategy: {
type: 'blue-green',
trafficSplit: {
blue: 100, // Current version
green: 0 // New version
},
switchCriteria: {
healthCheck: true,
performanceValidation: true,
userAcceptance: true
}
},
monitoring: {
metrics: ['latency', 'error-rate', 'accuracy', 'user-satisfaction'],
duration: 300000, // 5 minutes
thresholds: {
errorRate: 0.05,
latencyP95: 2000,
accuracyDrop: 0.02
}
}
})
// Gradually switch traffic
await deploymentManager.switchTraffic({
deploymentId: blueGreenDeployment.id,
trafficSplit: { blue: 50, green: 50 }
})
// Complete switch to green
await deploymentManager.completeSwitch({
deploymentId: blueGreenDeployment.id,
finalSplit: { blue: 0, green: 100 }
})
Canary Deployment
python
# Implement canary deployment strategy
canary_deployment = ants.llmops.canary_deployment
# Configure canary deployment
canary_config = canary_deployment.configure({
'model_id': 'customer-support',
'version': '2.1.0',
'strategy': {
'type': 'canary',
'stages': [
{'percentage': 5, 'duration': '1_hour'},
{'percentage': 25, 'duration': '2_hours'},
{'percentage': 50, 'duration': '4_hours'},
{'percentage': 100, 'duration': 'indefinite'}
]
},
'rollback_triggers': [
{'metric': 'error_rate', 'threshold': 0.05},
{'metric': 'latency_p95', 'threshold': 2000},
{'metric': 'user_satisfaction', 'threshold': 0.80}
],
'monitoring': {
'metrics': ['latency', 'error_rate', 'accuracy', 'cost'],
'alert_channels': ['email', 'slack', 'pagerduty']
}
})
# Start canary deployment
deployment = canary_deployment.start({
'config_id': canary_config.id,
'target_percentage': 5
})
print(f"Canary deployment started: {deployment.id}")
print(f"Current traffic: {deployment.current_percentage}%")
print(f"Status: {deployment.status}")
# Monitor and advance stages
while deployment.status == 'running':
metrics = canary_deployment.get_metrics(deployment.id)
if canary_deployment.should_advance(metrics):
deployment = canary_deployment.advance_stage(deployment.id)
print(f"Advanced to {deployment.current_percentage}% traffic")
else:
print("Metrics not meeting criteria, staying at current stage")
break
A/B Testing Deployment
typescript
// Implement A/B testing deployment
const abTestManager = await ants.llmops.abTestManager
const abTest = await abTestManager.createABTest({
name: 'customer-support-model-comparison',
variants: [
{
name: 'control',
modelId: 'customer-support',
version: '2.0.0',
trafficPercentage: 50
},
{
name: 'treatment',
modelId: 'customer-support',
version: '2.1.0',
trafficPercentage: 50
}
],
metrics: [
'accuracy',
'latency',
'cost-per-query',
'user-satisfaction',
'conversion-rate'
],
duration: '2_weeks',
statisticalSignificance: 0.95,
minimumSampleSize: 1000
})
// Monitor A/B test
const testResults = await abTestManager.getResults({
testId: abTest.id,
timeRange: 'last_7_days'
})
console.log('A/B Test Results:')
console.log(`Control accuracy: ${testResults.control.accuracy:.3f}`)
console.log(`Treatment accuracy: ${testResults.treatment.accuracy:.3f}`)
console.log(`Statistical significance: ${testResults.significance:.3f}`)
console.log(`Winner: ${testResults.winner}`)
Rollback Strategies
Automatic Rollback
python
# Configure automatic rollback
rollback_manager = ants.llmops.rollback_manager
# Set up rollback triggers
rollback_config = rollback_manager.configure({
'model_id': 'customer-support',
'triggers': [
{
'metric': 'error_rate',
'threshold': 0.05,
'duration': '5_minutes',
'action': 'immediate_rollback'
},
{
'metric': 'latency_p95',
'threshold': 2000,
'duration': '10_minutes',
'action': 'gradual_rollback'
},
{
'metric': 'user_satisfaction',
'threshold': 0.80,
'duration': '15_minutes',
'action': 'alert_and_rollback'
}
],
'rollback_strategy': {
'type': 'traffic_split',
'target_version': 'customer-support-2.0.0',
'rollback_speed': 'gradual' # immediate, gradual
},
'notifications': {
'channels': ['email', 'slack', 'pagerduty'],
'recipients': ['ml-team', 'oncall-engineer']
}
})
# Test rollback scenario
rollback_test = rollback_manager.test_rollback({
'config_id': rollback_config.id,
'scenario': 'error_rate_spike'
})
print("Rollback Test Results:")
print(f"Trigger detected: {rollback_test.trigger_detected}")
print(f"Rollback initiated: {rollback_test.rollback_initiated}")
print(f"Time to rollback: {rollback_test.time_to_rollback}s")
print(f"Traffic switched: {rollback_test.traffic_switched}%")
Manual Rollback
typescript
// Implement manual rollback procedures
const manualRollback = await ants.llmops.manualRollback
// Create rollback plan
const rollbackPlan = await manualRollback.createPlan({
currentVersion: 'customer-support-2.1.0',
targetVersion: 'customer-support-2.0.0',
reason: 'Performance degradation detected',
steps: [
{
step: 'stop-new-traffic',
description: 'Stop routing new traffic to v2.1.0',
estimatedTime: '2 minutes'
},
{
step: 'drain-existing-traffic',
description: 'Allow existing requests to complete',
estimatedTime: '5 minutes'
},
{
step: 'switch-to-previous-version',
description: 'Route all traffic to v2.0.0',
estimatedTime: '1 minute'
},
{
step: 'verify-rollback',
description: 'Confirm system is stable',
estimatedTime: '3 minutes'
}
],
validation: {
metrics: ['error-rate', 'latency', 'throughput'],
thresholds: {
errorRate: 0.02,
latencyP95: 1500
}
}
})
// Execute rollback
const rollbackExecution = await manualRollback.execute({
planId: rollbackPlan.id,
confirmSteps: true,
monitoring: true
})
console.log(`Rollback execution started: ${rollbackExecution.id}`)
console.log(`Current step: ${rollbackExecution.currentStep}`)
console.log(`Status: ${rollbackExecution.status}`)
Environment Management
Multi-Environment Deployment
python
# Manage multiple deployment environments
environment_manager = ants.llmops.environment_manager
# Configure environments
environments = environment_manager.configure({
'environments': [
{
'name': 'development',
'purpose': 'development_and_testing',
'auto_deploy': True,
'approval_required': False,
'monitoring': 'basic'
},
{
'name': 'staging',
'purpose': 'pre_production_testing',
'auto_deploy': False,
'approval_required': True,
'monitoring': 'comprehensive'
},
{
'name': 'production',
'purpose': 'live_production',
'auto_deploy': False,
'approval_required': True,
'monitoring': 'full',
'rollback_enabled': True
}
],
'promotion_pipeline': {
'development': 'staging',
'staging': 'production'
}
})
# Deploy to specific environment
deployment = environment_manager.deploy({
'model_id': 'customer-support',
'version': '2.1.0',
'environment': 'staging',
'approval_required': True
})
print(f"Deployment to {deployment.environment} initiated")
print(f"Deployment ID: {deployment.id}")
print(f"Status: {deployment.status}")
Environment Synchronization
typescript
// Synchronize environments
const environmentSync = await ants.llmops.environmentSync
// Sync configuration across environments
const syncResult = await environmentSync.sync({
sourceEnvironment: 'production',
targetEnvironments: ['staging', 'development'],
syncItems: [
'model-configurations',
'prompt-templates',
'monitoring-settings',
'alert-thresholds'
],
excludeItems: [
'production-secrets',
'production-urls'
]
})
console.log('Environment sync completed')
console.log(`Synced to ${syncResult.syncedEnvironments.length} environments`)
console.log(`Items synced: ${syncResult.syncedItems.length}`)
Deployment Monitoring
Real-time Deployment Monitoring
typescript
// Monitor deployment in real-time
const deploymentMonitor = await ants.llmops.deploymentMonitor
const monitoringConfig = await deploymentMonitor.configure({
deploymentId: 'customer-support-2.1.0-deployment',
metrics: [
'deployment-status',
'traffic-split',
'error-rate',
'latency',
'throughput',
'user-satisfaction'
],
alerts: [
{
metric: 'error-rate',
threshold: 0.05,
severity: 'critical',
channels: ['slack', 'pagerduty']
},
{
metric: 'latency',
threshold: 2000,
severity: 'warning',
channels: ['slack']
}
],
dashboard: {
enabled: true,
refreshInterval: 30,
widgets: ['metrics', 'traffic-split', 'alerts']
}
})
// Get deployment status
const status = await deploymentMonitor.getStatus({
deploymentId: 'customer-support-2.1.0-deployment'
})
console.log('Deployment Status:')
console.log(`Status: ${status.status}`)
console.log(`Traffic split: ${status.trafficSplit}`)
console.log(`Error rate: ${status.errorRate}`)
console.log(`Latency: ${status.latency}ms`)
Best Practices
1. Version Control
- Use semantic versioning for clear version identification
- Maintain detailed changelogs for each version
- Tag versions with meaningful metadata
- Keep previous versions available for rollback
2. Deployment Strategy
- Choose appropriate strategy based on risk tolerance
- Start with small traffic percentages for new versions
- Monitor closely during initial deployment
- Have rollback plans ready before deployment
3. Testing
- Automate testing at every stage
- Test in production-like environments before deployment
- Validate performance and accuracy metrics
- Test rollback procedures regularly
4. Monitoring
- Monitor key metrics continuously during deployment
- Set up alerts for critical thresholds
- Track user satisfaction and business metrics
- Maintain deployment dashboards for visibility
5. Rollback Planning
- Plan rollback procedures before deployment
- Test rollback scenarios regularly
- Maintain previous versions for quick rollback
- Document rollback procedures clearly
Integration with Other Components
FinOps Integration
- Cost tracking per deployment
- Budget monitoring during rollouts
- ROI analysis of deployment strategies
SRE Integration
- Reliability monitoring during deployments
- Incident response for deployment issues
- SLA tracking across versions
Security Posture Integration
- Security validation before deployment
- Compliance checking for new versions
- Audit trails for deployment activities