Topic 1 Question 30

Professional Machine Learning Engineer

Topic 1 Question 30
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?
- Create alerts to monitor for skew, and retrain the model.
- Perform feature selection on the model, and retrain the model with fewer features.
- Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service.
- Perform feature selection on the model, and retrain the model on a monthly basis with fewer features.
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コメント(16)
- A
  
  Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes. https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew
  
  👍 29
  celia202004102021/07/20
- A Data drift doesn't necessarily require feature reselection (e.g. by L2 regularization). https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges
  
  👍 5
  Paul_Dirac2021/06/24
- C. "A problem is said to be ill-posed if small changes in the given information cause large changes in the solution. This instability with respect to the data makes solutions unreliable because small measurement errors or uncertainties in parameters may be greatly magnified and lead to wildly different responses. […] The idea behind regularization is to use supplementary information to restate an ill-posed problem in a stable form."
  
  Reference: https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/
  
  👍 4
  p_inkfreud2021/06/02
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