# Ml Experimentation

Machine learning experimentation reference for model-experimentation conventions...

- Repository: https://github.com/microsoft/hve-core (1591 stars)
- Category: Data and ML
- License: CC-BY-4.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add microsoft/hve-core --skill ml-experimentation -g -a claude-code -y`
Codex: `npx skills add microsoft/hve-core --skill ml-experimentation -g -a codex -y`
Cursor: `npx skills add microsoft/hve-core --skill ml-experimentation -g -a cursor -y`
Gemini CLI: `npx skills add microsoft/hve-core --skill ml-experimentation -g -a gemini-cli -y`

## Description

Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# ML Experimentation Reference Pack

## Goal

Ground machine learning experimentation in the Microsoft CSE engineering playbook so that environment setup, repository structure, experiment tracking, dataset and model abstractions, evaluation flow, and production-readiness review are applied consistently and attributed accurately.

This pack is machine learning specific. It assumes a model is being trained, tracked, evaluated, or assessed for production. General experiment framing, hypothesis formation, and vetting belong to `experiment-design`.

## Inputs

* The ML experimentation setup under discussion: environments, repository layout, tracking framework, or evaluation flow
* The model under assessment and its training and evaluation history, when readiness is the question
* Existing dataset versioning, parameter tracking, and environment capture practice
* The engagement stage, since the production checklist has a lifecycle precondition

## Reference index

Read only the reference that matches the active concern.

The rest: https://github.com/microsoft/hve-core/blob/HEAD/.github/skills/data-science-engineering/ml-experimentation/SKILL.md