Learn what it means when a client asks to save an AI machine learning model in its current form without changes in this sample question from the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
You’ve run several versions of an AI machine learning model for a client that wants to anticipate repair needs for a machine on their assembly line. You’ve identified the version most likely to suit their needs. Now they’re asking you to save it in its current form without any changes.
What does that tell you they plan to do with it?
A. Print it for publication in a paper-based journal.
B. Stop using it and delete it.
C. Edit it in a Jupyter notebook using Python.
D. Continue using it with additional data.
Answer
When a client asks you to save a machine learning model in its current form without any changes, it most likely means they plan to:
D. Continue using it with additional data.
Explanation
A saved machine learning model can continue to learn, using additional data of the types for which it was initially configured. It can also be modified within Watson Studio.
The client’s request to save the AI machine learning model in its current form without any changes suggests that they plan to continue using it with additional data.
This indicates that the client is satisfied with the model’s performance and intends to deploy it in their operational environment, where it will continue to be used and possibly updated or retrained with new data as it becomes available.
Saving a model in its current state allows the client to deploy it into a production environment where it can be used to make predictions on new, real-world data. This is a common step after a model has been developed, trained, and validated to a satisfactory level of performance.
By requesting no changes be made, the client wants to “freeze” the model to preserve its current behavior and predictive power. Any edits or tweaks could alter how it functions.
The client can then feed the saved model additional data over time to generate the repair need predictions they are looking for. As it receives more data, the model can continue learning and adapting its predictions.
Printing the model for publication in a journal or deleting it are not logical next steps after identifying a promising version. Editing it further in a Jupyter Python notebook would conflict with the request to make no changes.
So in summary, asking to save a specific model version without changes strongly suggests the client’s intention is to put that model into practical use by having it process more data to meet their business need of anticipating machine repairs.
The key points are:
- Saving a specific model version in its current form allows deploying it to make predictions on new data
- Making no changes preserves the model’s predictive power that has been validated as suitable
- The model can then be given more data over time to generate the desired predictions
- This is a logical next step after model development, as opposed to printing, deleting or further editing it
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