Models failing in tutorial

appliable_ai — Thu, 11 Jun 2026 20:19:45 GMT

Hello,

I am following the "Get started: Build your first machine learning model on Databricks" tutorial, and am getting stuck on "Parallel training using Optuna".

When I Search runs to retrieve the best model, the following code fails as there are no models against the runs:

best_model_pyfunc = mlflow.pyfunc.load_model( 'runs:/{run_id}/model'.format( run_id=best_run.run_id ) )

When I go to the runs in Experiments, each model against the run says "Failed", with no other insight I can seem to find anywhere.

Why is the following code (direct from the full unaltered notebook provided by the tutorial) not successfully logging models against each run?

And how/where can I find out the cause behind this? I attempted to add logging inside the objective, and nothing showed in the cell. I know the code is running as each run logs test_auc against it, just not the model

def objective(trial): # Enable autologging on each worker mlflow.sklearn.autolog() with mlflow.start_run(nested=True): params = { 'n_estimators': trial.suggest_int('n_estimators', 20, 1000), 'learning_rate': trial.suggest_float('learning_rate', 0.05, 1.0, log=True), 'max_depth': trial.suggest_int('max_depth', 2, 5), } model_hp = sklearn.ensemble.GradientBoostingClassifier( random_state=0, **params ) model_hp.fit(X_train, y_train) predicted_probs = model_hp.predict_proba(X_test) # Tune based on the test AUC # In production, you could use a separate validation set instead roc_auc = sklearn.metrics.roc_auc_score(y_test, predicted_probs[:,1]) mlflow.log_metric('test_auc', roc_auc) # Negate the AUC because Optuna minimizes the objective by default return -roc_auc with mlflow.start_run(run_name='gb_optuna') as run: # Use the MLflow Tracking Server as the Optuna storage backend experiment_id = mlflow.active_run().info.experiment_id mlflow_storage = MlflowStorage(experiment_id=experiment_id) # MlflowSparkStudy distributes the tuning using Spark workers mlflow_study = MlflowSparkStudy( study_name="gb-optuna-tuning", storage=mlflow_storage, ) mlflow_study.optimize(objective, n_trials=32, n_jobs=4)

topic Models failing in tutorial in Machine Learning

Models failing in tutorial