Learn the key questions to consider during AI model deployment and monitoring, including performance across scenarios, failures, tracking progress, decay, and diversity. Prepare for the Google AI for Anyone certification exam with expert tips.
Table of Contents
Question
Which of the following questions will you consider during deployment and monitoring of your model? Select all that apply.
A. How does the model perform in different scenarios?
B. What are examples of failures, and why are these occurring?
C. How do I continue to track and improve the model’s progress?
D. Does the model decay over time more rapidly for one population than it does for another?
E. How is my model performing across my diverse user base?
Answer
A. How does the model perform in different scenarios?
B. What are examples of failures, and why are these occurring?
C. How do I continue to track and improve the model’s progress?
D. Does the model decay over time more rapidly for one population than it does for another?
E. How is my model performing across my diverse user base?
Explanation
When deploying and monitoring an AI model, it is crucial to consider all of the following questions:
A. How does the model perform in different scenarios?
It is essential to evaluate your model’s performance across various scenarios to ensure it can handle a wide range of inputs and situations. This helps identify potential weaknesses and areas for improvement.
B. What are examples of failures, and why are these occurring?
Identifying specific instances where your model fails and understanding the underlying reasons behind those failures is critical for refining and optimizing your model’s performance.
C. How do I continue to track and improve the model’s progress?
Continuously monitoring your model’s performance and implementing a system for tracking its progress over time allows you to identify trends, make necessary adjustments, and ensure ongoing improvement.
D. Does the model decay over time more rapidly for one population than it does for another?
Assessing whether your model’s performance deteriorates at different rates for various user populations helps you identify potential biases and ensures that your model remains fair and effective for all users.
E. How is my model performing across my diverse user base?
Evaluating your model’s performance across a diverse user base is crucial for ensuring that it can effectively serve the needs of all users, regardless of their background or characteristics.
By considering all of these questions during the deployment and monitoring stages, you can ensure that your AI model remains accurate, reliable, and equitable over time.
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