Your data science team has finished a series of hyperparameter experiments for a new demand-forecasting model. Next week you will hold an internal technical design review with software engineers who will deploy the model, data engineers who maintain the feature pipeline, and machine-learning researchers who will continue tuning. Which reporting artifact should you prioritize to communicate results effectively to this peer/professional stakeholder group and allow them to validate and reproduce your work?
A screenshot of the model's confusion matrix posted in the team chat with a brief statement of overall accuracy.
A one-page executive summary highlighting projected revenue impact while omitting technical details.
A version-controlled Jupyter Notebook that embeds the training code, hyperparameter settings, evaluation metrics, and diagnostic plots.
A marketing-style infographic focusing on user experience improvements without showing metrics or code.
Professional peer stakeholders are interested in the full technical context so they can inspect assumptions, rerun code, and iterate on the solution. A version-controlled Jupyter Notebook combines executable code, data-loading steps, hyperparameter values, evaluation metrics, and diagnostic plots in a single, shareable file, meeting reproducibility best practices and giving engineers everything they need to integrate or extend the model. Executive summaries, marketing infographics, or isolated screenshots lack the necessary detail, traceability, and transparency for this audience, so they do not satisfy the communication requirements for technical peers.
Ask Bash
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Why is version control important for a Jupyter Notebook in this scenario?
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What are hyperparameters, and why are they part of the reporting artifact?
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How do evaluation metrics and diagnostic plots help technical peers validate a model?