During a post-implementation review, your data-science team is told that its process documentation must satisfy these new constraints:
Remains fully human-readable in a terminal-only environment
Produces meaningful line-level diffs in Git pull requests
Allows the CI pipeline to automatically check for a Model-Version: header
Can be rendered to HTML for the corporate knowledge portal without proprietary tooling
Which documentation approach best meets all of these requirements while following life-cycle best practices for using Markdown?
Commit a README.md to each pipeline directory that begins with YAML front-matter containing the required Model-Version key and uses GitHub-flavored Markdown for the narrative content.
Keep the notes inside .ipynb notebook cells and rely on specialized notebook-diff tools during code review.
Export every tracking notebook to PDF and commit the PDFs to the repository after each change.
Write the documentation in a shared Confluence cloud wiki and store only the page URL in the repository.
Markdown files are plain-text, so they stay readable in any editor and generate clean, line-level diffs in Git. Adding YAML front-matter (for example, ---\nModel-Version: 2.3.1\n---) gives CI jobs a machine-parsable place to verify required metadata while still leaving the document human-readable. Static-site generators such as MkDocs, Jekyll, or Hugo can convert the same Markdown into HTML without proprietary software. PDF exports, external wikis, and raw .ipynb notebooks fail at least one stated requirement-either they are binary, depend on third-party services, or produce poor diffs.
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