redhat-developer / redhat-developer/rhdh-users-skill-pack
Retire the evaluation spike after coverage parity
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- Dominant language
- Python
- Stars
- 0
- Forks
- 2
- Avg merge
- 46m
- Merged PRs (30d)
- 12
Description
What to build
Retire the experimental rhdh-templates evaluation spike after proving that permanent evaluation sources cover its useful scenarios and that historical decisions remain discoverable.
Acceptance criteria
- A coverage comparison maps every retained spike scenario to a permanent behavior, routing, or uplift case and identifies anything intentionally excluded.
- Any AEH-generated case counted toward coverage parity was reviewed and promoted to a permanent case; unreviewed candidate output is excluded.
- Before spike retirement, at least one paired uplift pilot is rerun with MLflow enabled and its model/configuration metadata, per-case scores, artifacts, and traces are reviewed; MLflow's local database and artifacts remain uncommitted.
- Historical framework findings and artifact-policy rationale remain available in maintained documentation before spike files are removed.
- Spike removal is isolated from unrelated refactoring, and permanent local suites plus the repository test suite still pass afterward.
- Raw pilot artifacts, credentials, and local MLflow data are not added while retiring the spike.
Blocked by
- #15
Contributor guide
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.
Research direction
Start by reviewing blocked issue #15 and the retained evaluation scenarios. Map each scenario to permanent coverage, review AEH-generated cases, and preserve the framework findings and artifact-policy rationale in maintained documentation. Before removal, rerun the paired uplift pilot with MLflow enabled, then run the permanent local suites and repository test suite; done means the spike is removed without committing artifacts or local MLflow data.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning, testing-qa
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100