mlbookcamp-code/course-zoomcamp/05-deployment/02-pickle.md at master · alexeygrigorev/mlbookcamp-code · GitHub

5.2 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 being the model ready for prediction process use this code to save the model for later.
- ```
import pickle
with open('model.bin', 'wb') as f_out:
   pickle.dump((dcit_vectorizer, model), f_out)
f_out.close() ## After opening any file it's nessecery to close it
```
- In the code above we'll making a binary file named model.bin and writing the dict_vectorizer for one hot encoding and 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.
- ```
with open('mode.bin', 'rb') as f_in:  ## Note that never open a binary file you do not trust!
    dict_vectorizer, model = pickle.load(f_in)
f_in.close()
```
- With unpacking the model and the dict_vectorizer, We're able to again predict for new input values without training a new model by re-running the code.

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