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Saving and loading the model — Machine Learning Zoomcamp Module 5

In this session we'll cover the idea 'How to use the model in future without training and evaluating the code' - To save the model we made before there is an option using the pickle library: - First install the library with the command pip...

Machine Learning Zoomcamp › Module 5: Deploying Machine Learning Models › Saving and loading the model

Notes

In this session we’ll cover the idea “How to use the model in future without training and evaluating the code”

• To save the model we made before there is an option using the pickle library:
  • First install the library with the command pip install pickle-mixin if you don’t have it.
  • After training the model and making it ready for the prediction process, use this code to save the model for later.

  • import pickle
            with open('model.bin', 'wb') as f_out: # 'wb' means write-binary
                pickle.dump((dict_vectorizer, model), f_out)
            

  • In the code above we’ll make a binary file named model.bin, and write the dict_vectorizer for one hot encoding and the model as array in it. (We will save it as binary in case it wouldn’t be readable by humans)
  • To be able to use the model in future without running the code, We need to open the binary file we saved before.

  • import pickle
            with open('mode.bin', 'rb') as f_in: # very important to use 'rb' here, it means read-binary 
                dict_vectorizer, model = pickle.load(f_in)
            ## Note: never open a binary file you do not trust the source!
            

  • With unpacking the model and the dict_vectorizer, We’re able to predict again for new input values without training a new model by re-running the code.

Add notes from the video (PRs are welcome)

• Notes from Peter Ernicke

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