Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a function call for a function that calculates an attribute associated with a machine learning model. For each argument of the function call, the system identifies a parameter type of the argument, wherein the parameter type represents a type of data used with the machine learning model. The system also obtains a value accessor for retrieving features specific to the parameter type and obtains a value represented by the argument using the value accessor. The system then calculates the attribute by applying the function to the value and uses the attribute to execute the machine learning model.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a function call for a function that calculates an attribute associated with a machine learning model. For each argument of the function call, the system identifies a parameter type of the argument, wherein the parameter type represents a type of data used with the machine learning model. The system also obtains a value accessor for retrieving features specific to the parameter type and obtains a value represented by the argument using the value accessor. The system then calculates the attribute by applying the function to the value and uses the attribute to execute the machine learning model.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a model definition and a training configuration for a machine-learning model, wherein the training configuration includes a set of required features, a training technique, and a scoring function. Next, the system uses the model definition and the training configuration to load the machine-learning model and the set of required features into a training pipeline without requiring a user to manually identify the set of required features. The system then uses the training pipeline and the training configuration to update a set of parameters for the machine-learning model. Finally, the system stores mappings containing the updated set of parameters and the set of required features in a representation of the machine-learning model.