SnowPro Advanced Data Scientist

Question

A Data Scientist trained a supervised classification model on manually labeled training data. It performed well in production for several months, until model users reported degraded prediction accuracy.

The users demonstrated the degradation by collecting a set of recent predictions together with their actual labels.

What should the Data Scientist do?

Explanation

Production accuracy degradation with recent ground-truth labels indicates that the model should be retrained using labeled data representative of the current production population. Using the newly collected labeled data allows the model to learn patterns present in the changed distribution; changing hyperparameters, model class, or sampling only the original data does not incorporate those current patterns. AWS documentation describes training a new model with an expanded dataset when an underlying pattern absent from the previous training data has caused poor model performance.

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SnowPro Advanced Data Scientist

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