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Amazon MLA-C01 Exam - Topic 2 Question 22 Discussion
Topic #: 2
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?
When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.
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