# Model Versioning

> Model versioning is practice of tracking and managing different versions of machine learning models throughout their lifecycle. This skill covers versioning strategies, model registries, metadata mana

- Skill: `majiayu000/model-versioning` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/model-versioning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/model-versioning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/model-versioning

---


# Model Versioning

## Skill Profile
*(Select at least one profile to enable specific modules)*
- [ ] **DevOps**
- [x] **Backend**
- [ ] **Frontend**
- [ ] **AI-RAG**
- [ ] **Security Critical**

## Overview
Model versioning is practice of tracking and managing different versions of machine learning models throughout their lifecycle. This skill covers versioning strategies, model registries, metadata management, lineage tracking, artifact storage, promotion workflows, A/B testing, and model comparison tools.

## Why This Matters
- **Traceability**: Track model changes and lineage
- **Reproducibility**: Know which model produced which results
- **Rollback**: Revert to previous versions if needed
- **A/B Testing**: Compare model versions in production
- **Compliance**: Meet regulatory requirements for model tracking

## Core Concepts & Rules

### 1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs

### 2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment


## Inputs / Outputs / Contracts
#

## Skill Composition
* **Depends on**: None
* **Compatible with**: None
* **Conflicts with**: None
* **Related Skills**: None

## Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

```python
# Example implementation following best practices
def example_function():
    # Your implementation here
    pass
```


## Assumptions
- MLflow server is available
- Model artifacts can be stored
- Versioning strategy is agreed upon
- Sufficient metadata is available

## Compatibility
- Python: Full support
- MLflow: Full support
- Git: Full support
- PostgreSQL: Full support
- MinIO: Full support

## Test Scenario Matrix (QA Strategy)

| Type | Focus Area | Required Scenarios / Mocks |
| :--- | :--- | :--- |
| **Unit** | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| **Integration** | DB / API | All external API calls or database connections must be mocked during unit tests |
| **E2E** | User Journey | Critical user flows to test |
| **Performance** | Latency / Load | Benchmark requirements |
| **Security** | Vuln / Auth | SAST/DAST or dependency audit |
| **Frontend** | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |


## Technical Guardrails & Security Threat Model

### 1. Security & Privacy (Threat Model)
* **Top Threats**: Injection attacks, authentication bypass, data exposure
- [ ] **Data Handling**: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
- [ ] **Secrets Management**: No hardcoded API keys. Use Env Vars/Secrets Manager
- [ ] **Authorization**: Validate user permissions before state changes

### 2. Performance & Resources
- [ ] **Execution Efficiency**: Consider time complexity for algorithms
- [ ] **Memory Management**: Use streams/pagination for large data
- [ ] **Resource Cleanup**: Close DB connections/file handlers in finally blocks

### 3. Architecture & Scalability
- [ ] **Design Pattern**: Follow SOLID principles, use Dependency Injection
- [ ] **Modularity**: Decouple logic from UI/Frameworks

### 4. Observability & Reliability
- [ ] **Logging Standards**: Structured JSON, include trace IDs `request_id`
- [ ] **Metrics**: Track `error_rate`, `latency`, `queue_depth`
- [ ] **Error Handling**: Standardized error codes, no bare except
- [ ] **Observability Artifacts**:
    - **Log Fields**: timestamp, level, message, request_id
    - **Metrics**: request_count, error_count, response_time
    - **Dashboards/Alerts**: High Error Rate > 5%


## Agent Directives & Error Recovery
*(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)*

- **Thinking Process**: Analyze root cause before fixing. Do not brute-force.
- **Fallback Strategy**: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- **Self-Review**: Check against Guardrails & Anti-patterns before finalizing.
- **Output Constraints**: Output ONLY the modified code block. Do not explain unless asked.


## Definition of Done (DoD) Checklist

- [ ] Tests passed + coverage met
- [ ] Lint/Typecheck passed
- [ ] Logging/Metrics/Trace implemented
- [ ] Security checks passed
- [ ] Documentation/Changelog updated
- [ ] Accessibility/Performance requirements met (if frontend)


## Anti-patterns / Pitfalls

* ⛔ **Don't**: Log PII, catch-all exception, N+1 queries
* ⚠️ **Watch out for**: Common symptoms and quick fixes
* 💡 **Instead**: Use proper error handling, pagination, and logging


## Reference Links & Examples

* Internal documentation and examples
* Official documentation and best practices
* Community resources and discussions


## Versioning & Changelog

* **Version**: 1.0.0
* **Changelog**:
  - 2026-02-22: Initial version with complete template structure