What does this practice?

It practices aligning input handling between training and prediction.

The same model can behave differently when the incoming data shape changes.

Model serving package

Your task

Organize training and prediction code as a small package. The training function saves a model file, and the prediction function loads that file to predict new samples. What to do: complete train_and_save and load_and_predict. Note: use scikit-learn LogisticRegression. The save path is passed as an argument.

Ground rules

•Use joblib for save and load.
•The prediction function does not receive the training data again.

Not required

•Model registry server.
•Online API server.
•Experiment tracking UI.

▶About this challenge

Save and reload a model while keeping the data handling path consistent before prediction.

Serving code needs more than a model file; it also needs the incoming data shape to match what the model expects.

What does this practice?: It practices aligning input handling between training and prediction.
Why does it matter?: The same model can behave differently when the incoming data shape changes.