Serialize with Pickle and Joblib
Save and load sklearn models the safe way.
Serialize with Pickle and Joblib is a free MLOps Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Serialize a Model?
A trained model lives in memory and vanishes when your script ends. Serialization saves it to a file so you can load it again later. 💾
Meet Pickle
Python's built-in pickle module turns almost any object into bytes you can store on disk and rebuild later, exactly as it was.
Dump a Model to Disk
You open a file in binary write mode and call pickle.dump to write the trained model into it.
import pickle
with open('model.pkl', 'wb') as f:
pickle.dump(model, f)Load It Back
To restore the model, open the file in binary read mode and call pickle.load. You get the same object back, ready to predict.
with open('model.pkl', 'rb') as f:
model = pickle.load(f)Why Joblib Exists
Scikit-learn ships big NumPy arrays inside models. joblib stores those arrays far more efficiently than plain pickle does. 🚀
Save with Joblib
joblib gives you a clean one-liner: joblib.dump takes the object and a filename, no manual file handle needed.
import joblib
joblib.dump(model, 'model.joblib')Load with Joblib
Loading mirrors saving. Call joblib.load with the same filename and you get your trained estimator back.
model = joblib.load('model.joblib')
model.predict(X_test)Pickle's Big Warning
Never unpickle a file from an untrusted source. A malicious pickle can run arbitrary code the moment you load it. ⚠️
Versions Must Match
A pickle is tied to library versions. Loading a model under a different scikit-learn version can break or silently misbehave.
Pin What You Saved
Always record the exact library versions next to the file. Pinning your requirements means the saved model loads the same way tomorrow.
import sklearn
print(sklearn.__version__)Pickle or Joblib?
For scikit-learn models reach for joblib; for small, array-free Python objects, plain pickle is perfectly fine.
Quick Check
Time to test what you learned about serializing models.
Recap
You can now save and reload models with pickle and joblib, prefer joblib for sklearn, never load untrusted files, and pin versions. 🎉
Frequently asked questions
Is the “Serialize with Pickle and Joblib” lesson free?
Yes — the full text of “Serialize with Pickle and Joblib” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Serialize with Pickle and Joblib”?
Save and load sklearn models the safe way. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Serialize with Pickle and Joblib” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Serialize with Pickle and Joblib
- The MLflow Model Flavor
- Export to ONNX for Portability
- Define a Model Signature and Schema