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Guardado de modelos con joblib y pickle

Serializará un pipeline entrenado con joblib y pickle, lo volverá a cargar y verificará que las predicciones sean idénticas para confirmar que el proceso de ida y vuelta se realizó correctamente.

Guardado de modelos con joblib y pickle es una lección gratuita de Machine Learning Academy en CoddyKit. Esta es la lección 1 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de Machine Learning Academy, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Machine Learning Academy incluye 4 lecciones en total.

Partes de esta lección aún no han sido traducidas y se muestran en inglés.

Why Model Persistence Matters

Training a machine learning model is expensive: it can take minutes to hours and consumes significant compute. Model persistence saves the fitted model to disk so you can reload it instantly for inference without retraining. This is the bridge between the data science notebook and a production system — the serialised model file is the deployable artefact that data engineers package and serve.

What Gets Saved in a Model File?

When you serialise a fitted sklearn model or pipeline, the file captures: all fitted parameters (e.g., scaler mean and variance, tree structure, logistic regression coefficients), hyperparameter settings, and the Python class definition reference. It does NOT include the training data. Loading the file reconstructs a Python object ready to call predict immediately.

Saving with joblib.dump

joblib is the recommended serialisation tool for sklearn objects. It handles large NumPy arrays efficiently using memory mapping and supports transparent compression. The standard workflow is: train the model, dump it to a .joblib file, then load it in a separate script or service for inference.

import joblib
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer

X, y = load_breast_cancer(return_X_y=True)

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', LogisticRegression(C=1.0, max_iter=300))
])
pipe.fit(X, y)

# Save
joblib.dump(pipe, '/tmp/cancer_model.joblib')
print('Model saved to /tmp/cancer_model.joblib')

Loading with joblib.load

joblib.load deserialises the file and returns the exact fitted pipeline object. The loaded model has all of the same attributes — named_steps, fitted scaler parameters, classifier coefficients — as the original. You can immediately call predict, predict_proba, or score without any additional setup.

import joblib
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Load in a fresh context
model = joblib.load('/tmp/cancer_model.joblib')

predictions = model.predict(X_test[:5])
print('Predictions:', predictions)
print('Test accuracy:', model.score(X_test, y_test).round(4))

Using pickle for Serialisation

Python's standard library pickle module also serialises sklearn objects. Open files in binary mode ('wb' for write, 'rb' for read). The pickle.HIGHEST_PROTOCOL constant uses the most efficient available protocol. For small models or scripting contexts, pickle is perfectly adequate.

import pickle
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
model = LogisticRegression().fit(X, y)

# Save
with open('/tmp/iris_model.pkl', 'wb') as f:
    pickle.dump(model, f, protocol=pickle.HIGHEST_PROTOCOL)

# Load
with open('/tmp/iris_model.pkl', 'rb') as f:
    loaded = pickle.load(f)

print('Score:', loaded.score(X, y).round(4))
print('Coefficients shape:', loaded.coef_.shape)

Comparing joblib vs pickle File Sizes

For a large model like a RandomForest with 1000 trees, joblib's memory-mapped NumPy array storage is more efficient. The difference becomes especially pronounced when the model contains large parameter matrices. For small models (LogReg, SVM), the size difference is negligible.

import joblib
import pickle
import os
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000, n_features=20, random_state=0)
rf = RandomForestClassifier(n_estimators=100, random_state=0).fit(X, y)

# joblib
joblib.dump(rf, '/tmp/rf_model.joblib')

# pickle
with open('/tmp/rf_model.pkl', 'wb') as f:
    pickle.dump(rf, f)

print(f'joblib size: {os.path.getsize("/tmp/rf_model.joblib"):,} bytes')
print(f'pickle size: {os.path.getsize("/tmp/rf_model.pkl"):,} bytes')

Compression with joblib

Use joblib.dump(model, path, compress=3) to compress the file using zlib. Compression levels range from 1 (fast, larger) to 9 (slow, smallest). Level 3 is a practical default. For LZ4 compression (faster than zlib): compress=('lz4', 1). Load time slightly increases for compressed files but the network transfer and storage savings are often worth it.

import joblib
import os
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000, random_state=0)
rf = RandomForestClassifier(n_estimators=100, random_state=0).fit(X, y)

for level in [0, 3, 6, 9]:
    path = f'/tmp/rf_compress_{level}.joblib'
    joblib.dump(rf, path, compress=level)
    size = os.path.getsize(path)
    print(f'compress={level}: {size:,} bytes')

Verifying Round-Trip Consistency

After loading, always verify that the loaded model produces identical predictions to the original. This guards against silent corruption, version mismatches, or incomplete file writes. Compare predictions with np.array_equal on the same input.

import joblib
import numpy as np
from sklearn.datasets import load_iris
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

X, y = load_iris(return_X_y=True)
pipe = Pipeline([('sc', StandardScaler()), ('lr', LogisticRegression())]).fit(X, y)
original_preds = pipe.predict(X)

joblib.dump(pipe, '/tmp/verify_pipe.joblib')
loaded = joblib.load('/tmp/verify_pipe.joblib')
loaded_preds = loaded.predict(X)

if np.array_equal(original_preds, loaded_preds):
    print('Round-trip PASSED: predictions are identical.')
else:
    diff = (original_preds != loaded_preds).sum()
    print(f'Round-trip FAILED: {diff} different predictions!')

Version Pinning and Metadata

A model pickled with scikit-learn 1.2 may not load cleanly in scikit-learn 1.5 due to internal changes. Always store a metadata file alongside the model that records: sklearn version, Python version, training date, dataset version, and key metrics. This is your model card — the governance document that explains what the model is and how it was produced.

import json
import sklearn
import sys
from datetime import datetime

metadata = {
    'model_file': 'cancer_model.joblib',
    'sklearn_version': sklearn.__version__,
    'python_version': sys.version.split()[0],
    'training_date': datetime.utcnow().isoformat(),
    'dataset': 'breast_cancer',
    'test_accuracy': 0.9789,
    'features': 30,
    'algorithm': 'LogisticRegression'
}

with open('/tmp/cancer_model_metadata.json', 'w') as f:
    json.dump(metadata, f, indent=2)

print(json.dumps(metadata, indent=2))

Security: Never Load Untrusted Pickle Files

Critical security warning: both pickle and joblib can execute arbitrary Python code when loading. Never load a model file from an untrusted source — it could be a malicious payload disguised as a model. For models shared across organisations, consider safer formats: ONNX (Open Neural Network Exchange) is a standardised, inspectable format supported by most frameworks.

File Naming Conventions

Good naming conventions embed key information into the filename: algorithm, dataset, date, and metric. This makes the model registry self-documenting and prevents accidentally loading the wrong model version in production.

from datetime import date
from sklearn.metrics import accuracy_score
import joblib

# Example naming convention
dataset = 'breast_cancer'
algorithm = 'logreg'
test_acc = 0.9789
today = date.today().strftime('%Y%m%d')

filename = f'{dataset}_{algorithm}_{today}_acc{int(test_acc*100)}.joblib'
print('Model filename:', filename)
# e.g.: breast_cancer_logreg_20260620_acc97.joblib

# Load the model we saved earlier (demo)
model = joblib.load('/tmp/cancer_model.joblib')
print('Loaded OK')

Quick Check

Test your understanding of model serialisation with joblib and pickle from this lesson.

Lesson Recap

In this lesson you learned: joblib.dump and joblib.load serialise and restore fitted sklearn models efficiently, pickle works too but joblib is preferred for models with large NumPy arrays due to memory mapping, and always store a metadata file alongside the model recording library versions, training date, and key metrics for governance. Next up we design a versioning naming convention and metadata sidecar to track multiple model versions in a model registry.

Preguntas frecuentes

¿La lección «Guardado de modelos con joblib y pickle» es gratis?

Sí — el texto completo de «Guardado de modelos con joblib y pickle» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de Machine Learning Academy, actualiza a CoddyKit PRO. El curso de Machine Learning Academy incluye 4 lecciones en total.

¿Qué aprenderé en «Guardado de modelos con joblib y pickle»?

Serializará un pipeline entrenado con joblib y pickle, lo volverá a cargar y verificará que las predicciones sean idénticas para confirmar que el proceso de ida y vuelta se realizó correctamente. Practicas Machine Learning Academy con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.

¿Necesito experiencia previa para empezar Machine Learning Academy?

No se requiere experiencia previa. Machine Learning Academy en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 1 de 4.

¿Cuánto tiempo toma la lección «Guardado de modelos con joblib y pickle»?

La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.

¿Puedo escribir y ejecutar código en esta lección de Machine Learning Academy?

Sí. Cada lección de Machine Learning Academy incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.

Todas las lecciones de este curso

  1. Guardado de modelos con joblib y pickle
  2. Control de versiones de modelos: por qué importan los nombres de archivo y los metadatos
  3. Servir predicciones con un endpoint de FastAPI
  4. Supervisión de predicciones: registro de entradas y salidas
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