Ml Experimentation
Machine learning experimentation reference for model-experimentation conventions...
Install
npx skills add microsoft/hve-core --skill ml-experimentation -g -a claude-code -y- Repository
- microsoft/hve-core1.6k
- Category
- Data and ML
- Updated
- 3d ago
- License
- CC-BY-4.0
- Context cost
- Lightabout 1,492 tokens
- Runs scripts
- No, instructions only
What it does
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.
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.
