Serialization and Persistence Techniques

Saving and reloading models is essential for production workflows—training usually happens once in a given CI/CD/CT cycle, but prediction must happen repeatedly. In this recipe, we’ll demonstrate how to serialize models with both pickle and joblib, discuss security and versioning considerations, and show how third‑party formats like ONNX may be used for Python‑free environments.

Getting ready

You’ll need libraries to train a model and tools to persist it in multiple formats.

1. Load the libraries:

   import numpy as np
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.datasets import make_classification
   import pickle
   from joblib import dump, load

2. Train a classifier:

   X, y = make_classification(n_samples=500, n_features=15, random_state=2024)
   rf = RandomForestClassifier(n_estimators=10, random_state=2024)
   rf.fit(X, y)

Let’s apply both serialization techniques to convert our models into Python...