feat: Support mlflow as a board to read/write model pins
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
The issue names VetiverModel, boards, and an mlflow registry but no files or tests. Start by locating the board and model-pin entry points, then compare how model versions, metadata, dependencies, and API deployment are represented. Done means an mlflow-backed board can read and write model pins while preserving the existing user experience.
Written by the indexing model from the issue text.
Description
Vetiver and mlflow speak similar languages so it likely possible to view mlflow as a board to pull model versions into a VetiverModel and then deploy as an API. From the user's perspective, things would be more or less the same, but model itself and its metadata, deps, etc. would be coming from an mlflow registry.
- Dominant language
- Python
- Stars
- 71
- Forks
- 20
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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