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Question 8 - Professional Machine Learning Engineer discussion

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You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

A.
Keep the original test dataset unchanged even if newer products are incorporated into retraining
Answers
A.
Keep the original test dataset unchanged even if newer products are incorporated into retraining
B.
Extend your test dataset with images of the newer products when they are introduced to retraining
Answers
B.
Extend your test dataset with images of the newer products when they are introduced to retraining
C.
Replace your test dataset with images of the newer products when they are introduced to retraining.
Answers
C.
Replace your test dataset with images of the newer products when they are introduced to retraining.
D.
Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
Answers
D.
Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
Suggested answer: B

Explanation:

The test dataset is used to evaluate the performance of the ML model on unseen data. It should reflect the distribution of the data that the model will encounter in production. Therefore, if the retraining data includes new products, the test dataset should also be extended with images of those products to ensure that the model can generalize well to them. Keeping the original test dataset unchanged or replacing it entirely with images of the new products would not capture the diversity of the data that the model needs to handle. Updating the test dataset only when the evaluation metrics drop below a threshold would be reactive rather than proactive, and might result in poor user experience if the model fails to recognize the new products.Reference:

Continuous evaluation documentation

Preparing and using test sets

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asked 18/09/2024
Tania Trif
50 questions
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