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joblib ile İşlem Hattını Kaydetme ve Yükleme

Uygulanmış bir Pipeline'ı joblib.dump ile diske serileştirecek, yeniden eğitim yapmadan tahminlerde bulunmak için yeni bir Python oturumunda tekrar yükleyeceksiniz.

joblib ile İşlem Hattını Kaydetme ve Yükleme, CoddyKit'te ücretsiz bir Machine Learning Academy dersidir. Bu, 4 dersinin 4. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Machine Learning Academy öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Machine Learning Academy kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

Why Persist a Trained Pipeline?

Training a machine learning pipeline can take minutes or hours. Once fitted, you want to save it to disk so you can reload it later for predictions without re-training. Persistence is also essential for deployment: you train on a development machine and serve predictions on a production server. The saved file must include both the preprocessing steps and the model weights.

Two Serialisation Options: pickle and joblib

Python's built-in pickle module can serialise any Python object, including sklearn pipelines. joblib is a third-party library (bundled with scikit-learn) that is generally preferred for ML objects because it is more efficient for large NumPy arrays — using memory mapping instead of copying — and can compress the output file automatically.

import pickle
import joblib

# Both approaches work; joblib is recommended for sklearn objects
print('pickle version:', pickle.HIGHEST_PROTOCOL)
import sklearn
print('sklearn version:', sklearn.__version__)

Saving with joblib.dump

joblib.dump(obj, filename) serialises the pipeline to a file. You can optionally set compress=3 to use zlib compression (levels 1-9; 3 balances speed and size). The function returns a list of files created. For most pipelines, a single .pkl or .joblib file is created.

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

X, y = load_iris(return_X_y=True)

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', LogisticRegression(C=1.0, max_iter=200))
])
pipe.fit(X, y)

# Save
joblib.dump(pipe, '/tmp/iris_pipeline.joblib')
print('Pipeline saved!')

Loading with joblib.load

joblib.load(filename) deserialises the pipeline back into a Python object. The loaded pipeline is identical to the original: it has the same fitted scaler parameters (mean, variance) and the same model weights. You can call predict, predict_proba, or score immediately without re-fitting.

import joblib
from sklearn.datasets import load_iris
import numpy as np

X, y = load_iris(return_X_y=True)

# Load the saved pipeline
loaded_pipe = joblib.load('/tmp/iris_pipeline.joblib')

# Predict and verify
predictions = loaded_pipe.predict(X[:5])
print('Predictions:', predictions)
print('Test accuracy:', loaded_pipe.score(X, y).round(4))

Verifying Round-Trip Fidelity

After loading, confirm that the loaded pipeline produces identical predictions to the original. Any mismatch indicates a serialisation bug or a version incompatibility. A simple check is to compare predictions element-wise using np.array_equal.

import joblib
import numpy as np
from sklearn.datasets import load_iris

X, _ = load_iris(return_X_y=True)

# Reload and compare
loaded = joblib.load('/tmp/iris_pipeline.joblib')

# Reload the original reference predictions
# (in practice, save original predictions before reload)
original_preds = loaded.predict(X)  # use loaded as reference
loaded2 = joblib.load('/tmp/iris_pipeline.joblib')
reloaded_preds = loaded2.predict(X)

print('Predictions match:', np.array_equal(original_preds, reloaded_preds))

Compression Options in joblib

Large pipelines (e.g., with RandomForest of 1000 trees) can be hundreds of MB. Use joblib.dump(pipe, path, compress=3) to compress on the fly. Alternatively, specify the compressor explicitly: compress=('zlib', 3) or compress=('lz4', 1) for maximum speed. LZ4 is the fastest; zlib gives smaller files but is slower.

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

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

# Uncompressed
joblib.dump(pipe, '/tmp/pipe_raw.joblib')
# Compressed
joblib.dump(pipe, '/tmp/pipe_compressed.joblib', compress=3)

print('Raw size:', os.path.getsize('/tmp/pipe_raw.joblib'), 'bytes')
print('Compressed size:', os.path.getsize('/tmp/pipe_compressed.joblib'), 'bytes')

Using pickle as an Alternative

If joblib is not available, pickle works for sklearn pipelines. Use binary mode ('rb'/'wb') when opening the file. For small models or scripted one-off tools, pickle is perfectly fine; for production systems handling large NumPy arrays, joblib is strongly preferred.

import pickle
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

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

# Save with pickle
with open('/tmp/model.pkl', 'wb') as f:
    pickle.dump(pipe, f)

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

print('Loaded score:', loaded.score(X, y).round(4))

Version Compatibility Warnings

A critical production concern: a pipeline pickled with scikit-learn 1.2 may not load correctly in scikit-learn 1.5. Always record the library versions used at training time in a metadata file alongside the saved model. Use pip freeze > requirements.txt or record versions programmatically and store them next to the model file.

import sklearn
import numpy as np
import json
import os

metadata = {
    'sklearn_version': sklearn.__version__,
    'numpy_version': np.__version__,
    'model_file': 'iris_pipeline.joblib'
}

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

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

Loading a Pipeline in a Production Script

In a production service, the workflow is: load the pipeline once at startup (not per request), receive input features, preprocess with the pipeline's built-in transforms, and return predictions. Because the pipeline includes all preprocessing, the serving code does not need to know about scaling, encoding, or PCA — all of that is encapsulated in the saved object.

import joblib
import numpy as np

# At startup (once)
model = joblib.load('/tmp/iris_pipeline.joblib')

def predict(sepal_length, sepal_width, petal_length, petal_width):
    features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
    label = model.predict(features)[0]
    proba = model.predict_proba(features)[0]
    return {'label': int(label), 'confidence': round(float(proba.max()), 4)}

result = predict(5.1, 3.5, 1.4, 0.2)
print('Prediction result:', result)

Security Considerations with Pickled Models

Never load a pickle file from an untrusted source. Pickle files can execute arbitrary code on loading — this is a fundamental Python security constraint. For sharing models externally, consider format-specific safer alternatives: ONNX for cross-framework serialisation, or joblib files shared only within trusted infrastructure. Always verify the file checksum before loading.

import hashlib

def file_sha256(path):
    h = hashlib.sha256()
    with open(path, 'rb') as f:
        for chunk in iter(lambda: f.read(65536), b''):
            h.update(chunk)
    return h.hexdigest()

checksum = file_sha256('/tmp/iris_pipeline.joblib')
print('Model SHA-256:', checksum)

# In production: compare this checksum with the one stored in your model registry

Quick Load-Predict Sanity Test

A final best practice: include a short sanity test script in your model package that loads the pipeline, runs a known input, and asserts the expected output. Run this test in your CI/CD pipeline every time the model is promoted to production, confirming the file was not corrupted and the environment is compatible.

import joblib
import numpy as np

# Sanity test
model = joblib.load('/tmp/iris_pipeline.joblib')

# Known input (setosa): sepal_length=5.1, sepal_width=3.5, petal_length=1.4, petal_width=0.2
X_test = np.array([[5.1, 3.5, 1.4, 0.2]])
pred = model.predict(X_test)[0]

# Iris class 0 = setosa
assert pred == 0, f'Expected setosa (0) but got {pred}'
print('Sanity test PASSED — model predicts setosa correctly.')

Quick Check

Test your understanding of saving and loading pipelines from this lesson.

Lesson Recap

In this lesson you learned: joblib.dump and joblib.load save and restore a complete fitted pipeline including all preprocessing parameters, always record library versions alongside the saved model to ensure reproducible loading, and never load pickle files from untrusted sources as they can execute arbitrary code. Next up we tackle imbalanced datasets — detecting class imbalance and understanding why accuracy is a misleading metric in that setting.

Sıkça Sorulan Sorular

“joblib ile İşlem Hattını Kaydetme ve Yükleme” dersi ücretsiz mi?

Evet — “joblib ile İşlem Hattını Kaydetme ve Yükleme” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Machine Learning Academy kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Machine Learning Academy kursu toplamda 4 dersten oluşur.

“joblib ile İşlem Hattını Kaydetme ve Yükleme” dersinde ne öğreneceğim?

Uygulanmış bir Pipeline'ı joblib.dump ile diske serileştirecek, yeniden eğitim yapmadan tahminlerde bulunmak için yeni bir Python oturumunda tekrar yükleyeceksiniz. Machine Learning Academy ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Machine Learning Academy öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Machine Learning Academy, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 4. dersidir.

“joblib ile İşlem Hattını Kaydetme ve Yükleme” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Machine Learning Academy dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Machine Learning Academy dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

  1. İlk İşlem Hattınızı Oluşturma: Ölçekleyici ve Sınıflandırıcı
  2. İşlem Hattı İçinde ColumnTransformer
  3. Tam İşlem Hattını Çapraz Doğrulama ve Izgara Aramasıyla Değerlendirme
  4. joblib ile İşlem Hattını Kaydetme ve Yükleme
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