redhat-developer / redhat-developer/rhdh-users-skill-pack

Retire the evaluation spike after coverage parity

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Dominant language
Python
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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

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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