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.