During the hand-off phase of a machine-learning project, a senior data scientist must add process documentation so that incoming engineers can (1) trace every data transformation, (2) reproduce the experiment, and (3) understand the rationale behind model-selection decisions-without reading each line of code. Which practice MOST directly satisfies this requirement?
Schedule a CI/CD job that automatically retrains and redeploys the model every 24 hours with fresh data.
Embed structured Markdown narratives in project notebooks and link them to a data dictionary and model card stored in the repository.
Freeze package versions in a requirements.txt file and enforce a dedicated virtual environment for reruns.
Use feature branches and pull requests so reviewers can comment on each commit before merging to main.
Process documentation records the what and why of each step in the workflow. Embedding rich Markdown narratives inside notebooks-while linking to a central data dictionary and a model card-creates an immediately accessible, human-readable record of assumptions, transformation logic, and model intent. Markdown cells are explicitly recommended for documentation in Jupyter-based projects, and model cards or similar read-me artifacts serve as reference documentation for downstream consumers.
Freezing library versions in requirements.txt is essential for environment reproducibility but addresses dependency management rather than explanatory documentation. Git branches and pull-requests enable code review and version control, not process narration. Automating daily retraining belongs to CI/CD and model-ops concerns and likewise does not document the analytical reasoning. Therefore, embedding structured Markdown explanations linked to supporting artifacts is the only option that directly fulfills the stated documentation needs.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is a data dictionary?
Open an interactive chat with Bash
What is a model card, and why is it important?
Open an interactive chat with Bash
Why is Markdown recommended for documentation in Jupyter Notebooks?