import mlflow.pyfunc class MyModel(mlflow.pyfunc.PythonModel): def load_context(self, context): # Load model artifacts during initialization self.model = load_model(context.artifacts["model_file"]) def predict(self, context, model_input): # Access artifacts and metadata during prediction model_version = context.metadata.get("version") # Use the loaded model for prediction predictions = self.model.predict(model_input) return predictions Clearly, Context is for " Artifacts " and " Version " not Preprocessin etc.

The context parameter is typically used to provide additional context or resources to the model during prediction, such as preprocessing models or custom configuration files. This allows the model to make use of relevant information or dependencies necessary for accurate predictions.

The correct answer is E. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files