CERTIFIED-MACHINE-LEARNING-PROFESSIONAL · Question #48
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or comp
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Question
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model. Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?
Options
- AThe pvfunc model can be used to deploy models in a parallelizable fashion
- BThe same preprocessing logic will automatically be applied when calling fit
- CThe same preprocessing logic will automatically be applied when calling predict
- DThis approach has no impact when loading the logged Pvfunc model for downstream deployment
- EThere is no longer a need for pipeline-like machine learning objects
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