TP3 Partie 1 : log_model + Model Registry + promotion par alias

lab/modeling_ridge/cli.py

@@ -3,6 +3,7 @@ import logging
 import mlflow
 import pandas as pd
 import typer
+from mlflow.models import infer_signature
 from sklearn import linear_model
 from sklearn import metrics
 
@@ -17,6 +18,11 @@ logger = logging.getLogger(__name__)
 @app.command()
 def main(
     strategy: constants.ModellingStrategy = constants.ModellingStrategy.MIXED,
+    register: bool = typer.Option(
+        False,
+        "--register/--no-register",
+        help="Enregistrer chaque modele (par alpha) dans le Model Registry.",
+    ),
 ):
     training_file_path = constants.DATASET_DIR / "train.parquet"
     validation_file_path = constants.DATASET_DIR / "validation.parquet"
@@ -74,6 +80,18 @@ def main(
             ):
                 mlflow.log_metric(f"coef_{feature_name}", float(coefficient))
 
+            # Sauvegarde de l'artefact du modele (voir lab/modeling/cli.py).
+            signature = infer_signature(X_train, train_predictions)
+            mlflow.sklearn.log_model(
+                sk_model=model,
+                name="model",
+                signature=signature,
+                input_example=X_train.iloc[:5],
+                registered_model_name=(
+                    constants.REGISTERED_MODEL_NAME if register else None
+                ),
+            )
+
 
 if __name__ == "__main__":
     app()