Serialize and Deserialize ML Model

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Serializing an object means converting its state into a byte stream or another format that can be stored or transmitted. Deserialization, on the other hand, involves converting the serialized data back into an object with the same state as the original.

Using Joblib to Serialize and Deserialize a machine learning model

You can use the joblib module to serialize and deserialize Python objects, including scikit-learn models. Here's an example of how to do it:

1. Saving an object:
import joblib

# create an object
my_object = {"name": "John", "age": 30}

# serialize the object and save it to a file
joblib.dump(my_object, "my_object.joblib")
2. Loading an object:
import joblib

# load the serialized object from a file
my_object = joblib.load("my_object.joblib")

# print the loaded object
print(my_object)

In this example, we first create a Python dictionary object named my_object. We then serialize the object using the joblib.dump() method and save it to a file named my_object.joblib. To load the serialized object, we use the joblib.load() method and pass in the filename. The loaded object is stored in the my_object variable, and we can print it to verify that the deserialization was successful.

Similarly, you can use the joblib module to serialize and deserialize scikit-learn models. For example, to save a trained scikit-learn model, you can use the following code:

import joblib
from sklearn.linear_model import LogisticRegression

# create a scikit-learn model object
clf = LogisticRegression()
# train the model on some data
X = [[1, 2], [3, 4]]
y = [0, 1]
clf.fit(X, y)

# serialize the model and save it to a file
joblib.dump(clf, "my_model.joblib")

To load the saved scikit-learn model from a file, you can use the following code:

import joblib

# load the serialized model from a file
clf = joblib.load("my_model.joblib")

# use the loaded model to make predictions on new data
X_test = [[5, 6], [7, 8]]
y_pred = clf.predict(X_test)

# print the predictions
print(y_pred)

In this example, we first create a scikit-learn logistic regression model object named clf. We then train the model on some data and serialize it using the joblib.dump() method. To load the saved model, we use the joblib.load() method and pass in the filename. The loaded model is stored in the clf variable, and we can use it to make predictions on new data.

Published on 15-Mar-2023 by Ramzan Jamali

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