feat(ml): enregistrer le modèle dans le MLflow Model Registry
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@@ -181,7 +181,11 @@ def _log_to_mlflow(
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)
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mlflow.log_metrics({f"model_{cle}": valeur for cle, valeur in model_metrics.items()})
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mlflow.log_metrics({f"baseline_{cle}": valeur for cle, valeur in baseline_metrics.items()})
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mlflow.lightgbm.log_model(booster, name="model")
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mlflow.lightgbm.log_model(
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booster,
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name="model",
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registered_model_name="consumption-forecast-lightgbm",
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)
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mlflow.log_artifact(str(model_output))
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