Data Version Control

---
name: data-version-control
description: "Use when versioning data and models."
version: 1.0.0
author: Hermes Agent
license: MIT
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [data-science, dvc, version-control, mlops]
    related_skills: ['advanced-version-control-git']
---

## Overview
Version control data, models, and experiments for reproducible ML.

## When to Use
- "Data Version Control design and architecture"
- "Best practices for Data Version Control"
- "Data Version Control implementation and deployment"
- "Data Version Control optimization and monitoring"
- "Data Version Control troubleshooting and scaling"

## Key Concepts
1. Foundational concepts
2. Implementation approaches
3. Testing and validation

## Implementation Patterns
1. Define clear requirements and specifications
2. Choose appropriate tools and frameworks
3. Implement with modular, maintainable code
4. Write tests and automate verification
5. Document architecture and decisions
6. Monitor performance and iterate

## Common Pitfalls
1. **Not accounting for constraints** — resource or timeline limitations
2. **Ignoring industry standards** — not following established best practices
3. **Poor stakeholder alignment** — conflicting requirements
4. **Inadequate testing** — no validation of critical functions
5. **Not documenting decisions** — lost knowledge transfer

## Verification Checklist
- [ ] Requirements documented
- [ ] Standards reviewed
- [ ] Design validated
- [ ] Testing established
- [ ] Documentation complete