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Module 6 · Deep Learning + MLOps
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Notebooks → production
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📝 **Question:** Best path from prototype notebook to production? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
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📝 **Question:** Best path from prototype notebook to production? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
A
Drop the `.ipynb` straight into Papermill — its parameter injection layer turns notebooks into production-ready jobs.
B
Refactor to testable .py modules; unit tests; pinned deps; run via airflow/prefect/dagster.
C
Use `nbconvert --to script` and run the generated `.py` from cron — the conversion preserves all execution semantics.
D
Schedule the live notebook server with a cron-triggered webhook — Jupyter's REST API runs cells in the right order.
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