mlflow ยท diff
v1.0.0 to v1.0.0
6 added, 7 removed. Audit A to A.
---
name: mlflow
description: >
- Use this skill for MLflow run tracking, model registry workflows, artifact logging, and reproducible experiment lifecycle management.
- version: 1.0.0
- authors:
- - Marie-Lynne Block
- tags:
- - data-science
- - [TODO]
+ Use this skill for MLflow run tracking, model registry workflows,
+ artifact logging, and reproducible experiment lifecycle management.
+ metadata:
+ version: "1.0.0"
+ author: Marie-Lynne Block
+ tags: data-science, mlops
---
## What this skill does
[TODO] Define the specific workflow this skill standardizes, including default libraries,
quality checks, and expected deliverables.
## When to use it
[TODO] List concrete user intents and trigger phrases that should activate this skill.
## Instructions
1. Clarify the objective, data assumptions, and success metrics.
2. Execute a leakage-safe and reproducible workflow for this skill domain.
3. Validate outputs with diagnostics, edge-case checks, and documented caveats.
## Output format
- A concise plan of action
- Executable code or commands
- Validation summary with assumptions and risks
## Examples
### Example 1 - baseline workflow
**Input:** User asks for help in mlflow.
**Expected output:** A reproducible, validated workflow using the skill's core tools.
## Notes
- Prefer documented, stable APIs over experimental shortcuts.
- Record assumptions explicitly when data quality or labels are uncertain.