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MLS-C01 · Question #232

A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates…

The correct answer is A. Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the. "If the statistical nature of the data that your model receives while in production drifts away from the nature of the baseline data it was trained on, the model begins to lose accuracy in its predictions. Amazon SageMaker Model Monitor uses rules to detect data drift and…

Machine Learning Implementation and Operations

Question

A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future. Which solution will meet these requirements with the LEAST amount of operational overhead?

Options

  • AUse Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the
  • BUse Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the
  • CUse Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon
  • DUse Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon

How the community answered

(37 responses)
  • A
    70% (26)
  • B
    16% (6)
  • C
    3% (1)
  • D
    11% (4)

Explanation

"If the statistical nature of the data that your model receives while in production drifts away from the nature of the baseline data it was trained on, the model begins to lose accuracy in its predictions. Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens." https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-quality.html

Topics

#Model Monitoring#Data Drift#Amazon SageMaker Model Monitor#MLOps

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