Model Lifecycle Manager

Overview

Manage the full lifecycle of AI/ML models from deployment through retirement. Track versions, monitor for drift, schedule retraining, and maintain clear lineage.

Core principle: Production models require active management. Deploy and forget leads to degraded performance and silent failures.

When to Use

•New model deployed to production
•Setting up monitoring for existing models
•Planning retraining cadence
•Investigating model performance issues
•Preparing for model retirement

Output Format

yaml

model_lifecycle:
  model_id: "[Unique identifier]"
  model_name: "[Human-readable name]"
  version: "[Current version]"
  
  metadata:
    purpose: "[What the model does]"
    owner: "[Team/person responsible]"
    deployed_date: "[YYYY-MM-DD]"
    framework: "[TensorFlow | PyTorch | Scikit-learn | etc.]"
    model_type: "[Classification | Regression | LLM | etc.]"
    training_data_snapshot: "[Reference to training data version]"
  
  deployment:
    environment: "[Production | Staging | etc.]"
    endpoint: "[API endpoint or service]"
    infrastructure: "[Cloud service, instance type]"
    scaling_config:
      min_instances: "[N]"
      max_instances: "[N]"
      auto_scale_metric: "[What triggers scaling]"
  
  monitoring:
    performance_metrics:
      - metric: "[Accuracy | Precision | Latency | etc.]"
        current: "[Value]"
        threshold: "[Alert if below/above]"
        measurement_frequency: "[Real-time | Hourly | Daily]"
    
    drift_detection:
      data_drift:
        method: "[PSI | KS-test | etc.]"
        features_monitored: ["[Feature 1]", "[Feature 2]"]
        threshold: "[Trigger value]"
        check_frequency: "[Daily | Weekly]"
      
      concept_drift:
        method: "[Performance degradation | Label drift]"
        window: "[Time period]"
        threshold: "[% change to trigger]"
    
    alerts:
      - condition: "[What triggers alert]"
        severity: "[Critical | Warning | Info]"
        channel: "[Slack | PagerDuty | Email]"
        runbook: "[Link to response procedure]"
  
  retraining:
    trigger_conditions:
      - trigger: "[Drift threshold exceeded]"
        action: "Initiate retraining pipeline"
      - trigger: "[Scheduled: Monthly]"
        action: "Evaluate if retraining needed"
    
    pipeline:
      location: "[CI/CD pipeline reference]"
      data_sources: ["[Training data locations]"]
      validation_requirements:
        - "[Must meet baseline accuracy]"
        - "[A/B test against current model]"
    
    approval_process:
      auto_deploy_if: "[Conditions for automatic deployment]"
      manual_review_if: "[Conditions requiring human review]"
      approvers: ["[Role/person]"]
  
  versioning:
    current_version: "[X.Y.Z]"
    previous_versions:
      - version: "[X.Y.Z-1]"
        deployed: "[Date range]"
        retired_reason: "[Why replaced]"
    
    rollback:
      available_versions: ["[List]"]
      rollback_procedure: "[Steps]"
      rollback_time: "[Expected duration]"
  
  retirement:
    status: "[Active | Deprecated | Retired]"
    deprecation_date: "[If applicable]"
    replacement: "[Successor model if any]"
    data_retention: "[How long to keep artifacts]"

Lifecycle Stages

code

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ Development │───>│  Staging    │───>│ Production  │
└─────────────┘    └─────────────┘    └─────────────┘
                                            │
        ┌───────────────────────────────────┤
        │                                   │
        ▼                                   ▼
┌─────────────┐                     ┌─────────────┐
│  Retrain    │<────────────────────│  Monitor    │
└─────────────┘    (drift detected) └─────────────┘
        │
        ▼
┌─────────────┐
│  Validate   │───> (pass) ───> Promote to Production
└─────────────┘
        │
        └───> (fail) ───> Investigate, iterate

Drift Detection

Data Drift

Input feature distributions change:

Method	When to Use	Threshold
PSI (Population Stability Index)	Categorical features	>0.2 significant
KS Test	Continuous features	p < 0.05
Chi-Square	Categorical distributions	p < 0.05

Concept Drift

Relationship between inputs and outputs changes:

Indicator	Detection Method
Accuracy degradation	Compare to baseline over rolling window
Prediction distribution shift	Monitor output distribution
Ground truth lag	Compare delayed labels to predictions

Retraining Triggers

Trigger Type	Condition	Response
Scheduled	Time-based (monthly/quarterly)	Evaluate metrics, retrain if needed
Performance	Accuracy drops below threshold	Initiate retraining pipeline
Data drift	Feature distributions shift	Evaluate impact, retrain if significant
Manual	Business request or known issue	Ad-hoc retraining

Version Control

Semantic Versioning for Models

code

v[MAJOR].[MINOR].[PATCH]

MAJOR: Architecture change, breaking API change
MINOR: Retraining with new data, feature additions
PATCH: Bug fixes, hyperparameter tuning

Artifacts to Version

•Model weights/files
•Training configuration
•Feature engineering code
•Training data snapshot reference
•Evaluation metrics

Monitoring Dashboard

yaml

dashboard_components:
  health_summary:
    - "Model status (healthy/degraded/down)"
    - "Last prediction timestamp"
    - "Error rate (last 24h)"
  
  performance:
    - "Primary metric trend (30 days)"
    - "Latency p50/p95/p99"
    - "Throughput (predictions/sec)"
  
  drift:
    - "Data drift score by feature"
    - "Prediction distribution"
    - "Drift trend over time"
  
  operations:
    - "Resource utilization"
    - "Cost per prediction"
    - "Version history"

Checklist

• Model registered with unique ID
• Metadata complete (owner, purpose, framework)
• Monitoring configured with alerts
• Drift detection enabled
• Retraining pipeline tested
• Rollback procedure documented
• Retirement plan defined