You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

The correct answer is D. Compare the mean average precision across the models using the Continuous Evaluation. Comparing the mean average precision across the models using the Continuous Evaluation feature is the best way to compare the models' performance over time. Mean average precision is a measure of a model's ability to rank positive examples higher than negative examples. The Conti

Submitted by rania.sa· Apr 18, 2026Monitoring, optimizing, and maintaining ML solutions

Question

You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

Options

ACompare the loss performance for each model on a held-out dataset.
BCompare the loss performance for each model on the validation data.
CCompare the receiver operating characteristic (ROC) curve for each model using the What-If
DCompare the mean average precision across the models using the Continuous Evaluation

How the community answered

(19 responses)

A
5% (1)
B
5% (1)
C
11% (2)
D
79% (15)

Explanation

Comparing the mean average precision across the models using the Continuous Evaluation feature is the best way to compare the models' performance over time. Mean average precision is a measure of a model's ability to rank positive examples higher than negative examples. The Continuous Evaluation feature automatically evaluates the models' performance on new data as it becomes available. https://cloud.google.com/ai-platform/prediction/docs/continuous-evaluation#how_it_works

Topics

#Model Monitoring#Image Classification Metrics#Continuous Evaluation#Deployed Model Comparison

Community Discussion

No community discussion yet for this question.

Full PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice