Learn why saving machine learning models as Jupyter Notebook (ipynb) files enables code editing and reuse with new datasets. Expert tips for the IBM AI certification exam.
Table of Contents
Question
Why would you save your model as a Jupyter Notebook (ipynb) file?
Select the two that apply.
A. To be able to edit the code and algorithms
B. To encrypt the code for security purposes
C. To secure the model against changes
D. To be able to use it with a different data set
Answer
The two correct reasons to save your machine learning model as a Jupyter Notebook (.ipynb) file are:
A. To be able to edit the code and algorithms
D. To be able to use it with a different data set
Explanation
You would save your work as a model in order to continue training and using it with different data sets. Saving the model as a Jupyter Notebook will also allow you to edit the code or algorithms.
Saving a model as a Jupyter Notebook preserves all the code, markdown text, and visualizations in an interactive document format. This allows you to go back later and view, edit, and re-run the code that built and trained the model. Being able to see and modify the underlying algorithms is useful for iterating and improving the model.
Additionally, saving the model code in a notebook makes it easy to swap in a new dataset and re-run the notebook. This lets you reuse and adapt the model for different scenarios and datasets without having to rebuild it from scratch.
The other two options are incorrect:
B. Jupyter Notebooks do not provide encryption for security. While you can restrict access to sensitive .ipynb files, the format itself does not secure the contents.
C. Saving as a .ipynb file does not inherently secure the model against changes, since the code remains fully editable. There are ways to protect cells from editing, but that is not the default.
So in summary, the key benefits of saving models as Jupyter Notebooks are being able to view/edit the code later and reuse the model with different input data. The interactive notebook format provides flexibility to iterate and adapt the model as needed.
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