Saving and Loading Your Model
Persist with joblib for reuse.
Saving and Loading Your Model is a free NLP Academy lesson on CoddyKit — lesson 3 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 NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Save a Model?
Training can take minutes or hours. Saving the fitted model lets you reuse it instantly, with no need to retrain every time. 💾
Serialization in Plain Words
Serialization turns your in-memory model into bytes you can write to disk. Loading reverses it back into a working object.
Meet joblib
For scikit-learn models, joblib is the recommended tool. It handles the large numpy arrays inside your model efficiently.
import joblibSave With dump
Call dump with your fitted pipeline and a filename. That single line writes the entire model to disk.
joblib.dump(pipe, "models/sentiment.joblib")Load With load
Later, restore the model with load. You get back the exact same object, ready to predict right away.
model = joblib.load("models/sentiment.joblib")Save the Whole Pipeline
Always save the full pipeline, not just the classifier. The vectorizer must travel with it, or new text will not be encoded correctly.
joblib vs pickle
Plain pickle works too, but joblib is faster for the big arrays scikit-learn models contain. Prefer joblib for these models.
Pin the Version
A model saved with one scikit-learn version may fail to load on another. Record the library version beside your saved file.
Trust Only Your Files
Loading runs code, so a malicious file can harm you. Only load model files from sources you fully trust. ⚠️
Keep Models Out of Git
Saved models are large binaries. Add the models folder to gitignore and store big files in proper storage instead.
# .gitignore
models/Verify After Loading
After loading, run a quick prediction on a known example. If the output looks right, your saved model is intact and ready.
print(model.predict(["this movie was great"]))Quick Check
Think about which object you must persist to disk.
Recap
You used joblib to dump and load the full pipeline, pinned the version, kept models out of git, and verified with a test prediction. 🎉
Frequently asked questions
Is the “Saving and Loading Your Model” lesson free?
Yes — the full text of “Saving and Loading Your Model” is free to read here on the web, and the NLP 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 NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Saving and Loading Your Model”?
Persist with joblib for reuse. You practise NLP 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 NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Saving and Loading Your Model” 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 NLP Academy lesson?
Yes. Every NLP 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
- Structuring a Real NLP Project
- scikit-learn Pipelines End to End
- Saving and Loading Your Model
- Predicting on Brand-New Text