Packaging, Documenting, and Presenting the Final Model
Learners will serialise the winning pipeline, write a model card documenting training data, performance, limitations, and fairness considerations, and deliver a five-minute demo.
Why Packaging and Documentation Matter
A model that lives only in a Jupyter notebook is not a deliverable — it is a prototype. Packaging means serialising the trained pipeline into a portable artifact that can be loaded and used without the original training code. Documentation means producing a model card that records what the model does, what data it was trained on, how well it performs, where it might fail, and who should use it. Together, packaging and documentation are what transform research into responsible production software.
Serialising the Final Pipeline with joblib
joblib.dump serialises a fitted scikit-learn Pipeline (including all preprocessors and the model) to a single file. This file can be loaded in any Python environment with the same scikit-learn version installed. Use a structured filename that encodes the project, model type, training date, and performance metric so you can identify any artifact without reading its metadata sidecar.
import joblib
import json
from datetime import date
# Save the fitted pipeline
model_filename = f'churn_xgboost_{date.today().isoformat()}_auc0883.pkl'
joblib.dump(best_pipeline, model_filename)
print(f'Saved pipeline: {model_filename}')
# Verify round-trip
loaded_pipeline = joblib.load(model_filename)
y_reloaded = loaded_pipeline.predict_proba(X_test[:5])[:, 1]
y_original = best_pipeline.predict_proba(X_test[:5])[:, 1]
print('Predictions match after reload:', all(y_reloaded == y_original))All lessons in this course
- Project Scoping: Defining the Problem and Success Criteria
- Data Wrangling and Exploratory Data Analysis
- Model Selection Tournament: Compare Five Algorithms
- Packaging, Documenting, and Presenting the Final Model