Building Reproducible ML Pipelines
sklearn Pipeline, persisting pipelines with joblib, parameterized runs with config files.
Why Reproducibility?
A result you cannot reproduce is not science, it is luck. Reproducible pipelines ensure the same data and config always yield the same model, which is essential for debugging, audits, and teamwork.
The sklearn Pipeline
A scikit-learn Pipeline chains preprocessing and the model into one object, so the exact same transforms applied in training are applied at inference, eliminating train/serve skew.
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
pipe = Pipeline([
("scaler", StandardScaler()),
("model", RandomForestClassifier(n_estimators=200))
])
pipe.fit(X_train, y_train)All lessons in this course
- Experiment Tracking with MLflow
- Model Registry and Versioning
- Building Reproducible ML Pipelines
- Monitoring Model Performance in Production