Write an ML experiment tracking setup
Track ML experiments with MLflow
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Set up MLflow experiment tracking for a [model type] training script. Log: hyperparameters, training/validation metrics per epoch, feature importance, model artifacts, input data hash, and environment info. Create a comparison view across runs and set up a model registry with staging/production stages.
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Conseil : pour la traçabilité, loggez aussi un hash des données (`mlflow.log_input` ou une métrique `data_hash`) et les versions des librairies via `mlflow.log_env`. Pour le registre, préférez `client.transition_model_version_stage` avec des étapes explicites. La comparaison se fait simplement avec `mlflow.search_runs` qui retourne un DataFrame prêt à analyser.
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