I followed the Text Classification sample to create a TF-IDF experiment using the embeded R script. And store the
created dictionary into a storage Blob in training experiment. And read it from the Blob in predictive web service. Everything works fine when manually run it.
Then I created a new endpoint for retraining using Python program from local. When retraining the training experiment and overwrite the trained model, it works also perfectly. But when run the predictive experiment, it said 'The
data set being scored must contain all features used during training'. The error was reported by Score Model. I checked that the storage Blob had really been updated.
I guess if the storage Blob was not refreshed in the new endpoint. So the features between dictionary and trained model cannot be matched. But how to fix it, or is there a better solution for TF-IDF creation?