Learn Model Versioning

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As you move further into advanced ML deployment, you will quickly realize that keeping track of your models is not only helpful but absolutely necessary. Model versioning refers to the practice of systematically managing and identifying different iterations of your machine learning models. This is essential for several reasons: it ensures that you can reproduce results, trace how a model was built or modified, and roll back to previous versions if something goes wrong in production. Without proper versioning, you risk losing track of which model is running in production, which data it was trained on, and what parameters or code changes led to its current state. This can result in confusion, errors, and a lack of accountability—especially when multiple team members are involved or when models are updated frequently.


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import pickle
from sklearn.linear_model import LogisticRegression

# Example: training a simple model
X = [[0, 0], [1, 1]]
y = [0, 1]
model_v1 = LogisticRegression().fit(X, y)

# Save version 1 of the model
with open("model_v1.pkl", "wb") as f:
    pickle.dump(model_v1, f)

# Later, you might retrain or update your model
model_v2 = LogisticRegression(C=0.5).fit(X, y)

# Save version 2 of the model
with open("model_v2.pkl", "wb") as f:
    pickle.dump(model_v2, f)

# To load a specific version
with open("model_v1.pkl", "rb") as f:
    loaded_model = pickle.load(f)
    print("Loaded model version 1:", loaded_model)

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Section 1. Chapter 6

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Section 1. Chapter 6