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A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?
A)
B)
C)
D)
E)
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?
Which of the following is a probable response to identifying drift in a machine learning application?
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?
