Model Inventory Manager

Overview

Maintain a comprehensive registry of all AI/ML models in the organization. Track metadata, lineage, risk levels, and compliance status for governance and operations.

Core principle: You can't govern what you can't see. A complete model inventory is foundational for AI governance.

When to Use

•Establishing AI governance
•Regulatory compliance (EU AI Act, etc.)
•Risk assessments
•Audit preparation
•Portfolio planning

Output Format

yaml

model_inventory:
  version: "[Version]"
  last_updated: "[YYYY-MM-DD]"
  
  models:
    - model_id: "[Unique ID]"
      name: "[Model name]"
      version: "[Current version]"
      
      classification:
        type: "[Classification | Regression | LLM | etc.]"
        purpose: "[What it does]"
        domain: "[Business domain]"
        risk_tier: "[High | Medium | Low]"
      
      ownership:
        business_owner: "[Name/Team]"
        technical_owner: "[Name/Team]"
        data_owner: "[Name/Team]"
      
      lifecycle:
        status: "[Development | Staging | Production | Deprecated | Retired]"
        deployed_date: "[YYYY-MM-DD]"
        last_updated: "[YYYY-MM-DD]"
        next_review: "[YYYY-MM-DD]"
      
      technical:
        framework: "[TensorFlow | PyTorch | etc.]"
        hosting: "[Where deployed]"
        dependencies: ["[Dependency]"]
        performance_metrics:
          - metric: "[Metric]"
            value: "[Value]"
            threshold: "[Acceptable]"
      
      data:
        training_data: "[Description/reference]"
        features: ["[Key features]"]
        data_sensitivity: "[Classification]"
        data_retention: "[Policy]"
      
      governance:
        bias_audit: 
          completed: "[Date]"
          result: "[Pass | Conditional | Fail]"
        security_review:
          completed: "[Date]"
          result: "[Approved | Pending | Issues]"
        privacy_impact:
          required: [true | false]
          completed: "[Date if applicable]"
        explainability:
          method: "[How explained to users]"
          documentation: "[Link]"
      
      compliance:
        regulations: ["[Applicable regulation]"]
        compliance_status: "[Compliant | In progress | Gap]"
        audit_trail: "[Where to find logs]"
      
      incidents:
        - date: "[Date]"
          type: "[Incident type]"
          resolution: "[How resolved]"
      
      documentation:
        model_card: "[Link]"
        technical_docs: "[Link]"
        user_guide: "[Link]"
  
  summary:
    total_models: "[N]"
    by_status:
      production: "[N]"
      development: "[N]"
      deprecated: "[N]"
    by_risk_tier:
      high: "[N]"
      medium: "[N]"
      low: "[N]"
    reviews_due: "[N in next 30 days]"
    compliance_gaps: "[N]"

Risk Tiering

Risk Classification

Tier	Criteria	Governance Requirements
High	Decisions affecting individuals, safety-critical, regulated	Full documentation, bias audit, human oversight, annual review
Medium	Significant business impact, customer-facing	Standard documentation, periodic review
Low	Internal productivity, limited impact	Basic registration, light oversight

Risk Assessment Questions

yaml

risk_questions:
  - question: "Does it make decisions about individuals?"
    high_risk_if: "Yes"
  
  - question: "Is it safety-critical?"
    high_risk_if: "Yes"
  
  - question: "Is it subject to specific regulation?"
    high_risk_if: "Yes"
  
  - question: "Is it customer-facing without human review?"
    high_risk_if: "Yes"
  
  - question: "Does it process sensitive data?"
    medium_risk_if: "Yes"

Required Metadata

Minimum for All Models

yaml

required_fields:
  - "model_id"
  - "name"
  - "purpose"
  - "business_owner"
  - "technical_owner"
  - "status"
  - "risk_tier"
  - "data_sensitivity"

Additional for High-Risk

yaml

high_risk_required:
  - "training_data_documentation"
  - "bias_audit_results"
  - "explainability_method"
  - "human_oversight_process"
  - "incident_response_plan"
  - "regulatory_compliance_mapping"

Model Card Template

yaml

model_card:
  model_details:
    name: "[Name]"
    version: "[Version]"
    type: "[Type]"
    developers: "[Who built it]"
    date: "[When]"
  
  intended_use:
    primary_use: "[What it's for]"
    users: "[Who uses it]"
    out_of_scope: "[What it's NOT for]"
  
  training_data:
    datasets: "[What was used]"
    preprocessing: "[How data was prepared]"
    limitations: "[Known data gaps]"
  
  performance:
    metrics:
      - metric: "[Metric]"
        value: "[Value]"
        test_set: "[What dataset]"
    performance_by_group:
      - group: "[Subgroup]"
        performance: "[Value]"
  
  ethical_considerations:
    fairness: "[Assessment]"
    privacy: "[Considerations]"
    security: "[Considerations]"
  
  limitations:
    known_limitations: ["[Limitation]"]
    recommendations: ["[How to mitigate]"]

Governance Reporting

Executive Dashboard

yaml

governance_dashboard:
  model_count:
    total: "[N]"
    production: "[N]"
    high_risk: "[N]"
  
  compliance:
    fully_compliant: "[%]"
    gaps: "[N models with gaps]"
    overdue_reviews: "[N]"
  
  trends:
    new_models_this_quarter: "[N]"
    retired_this_quarter: "[N]"
    incidents_this_quarter: "[N]"
  
  attention_required:
    - "[Model X] - Review overdue"
    - "[Model Y] - Bias audit needed"

Inventory Maintenance

Triggers for Update

Event	Action
New model deployed	Add to inventory
Model updated	Update version, metadata
Incident occurs	Log in incident history
Review completed	Update audit dates, results
Model retired	Update status, retention

Review Schedule

Model Tier	Review Frequency
High risk	Quarterly
Medium risk	Semi-annually
Low risk	Annually

Checklist

• All production models registered
• Risk tiers assigned
• Owners identified
• Required metadata complete
• High-risk models have model cards
• Review schedule established
• Governance reporting active
• Retirement process defined