/tmp/train.py:39: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.
  df = pd.read_sql("SELECT customer_id, age, income, debt_ratio, default_flag FROM demo_customers", conn)
/home/dagster/.local/lib/python3.12/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details.
  warnings.warn(
/home/dagster/.local/lib/python3.12/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details.
  warnings.warn(
[step3] Trino fetch
[step3] rows=10000, default_rate=0.153
[step3] accuracy=0.8475, roc_auc=0.4972
[step3] experiment exists id=1
[step3] run_id=7dc5896e2fb744608004bdab8ad505e7
[step3] artifact_uri=s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts
[step3] saved local model to /tmp/tmpz5jrji5d/model
[step3] uploaded s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model/serving_input_example.json
[step3] uploaded s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model/conda.yaml
[step3] uploaded s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model/requirements.txt
[step3] uploaded s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model/input_example.json
[step3] uploaded s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model/model.pkl
[step3] uploaded s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model/python_env.yaml
[step3] uploaded s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model/MLmodel
[step3] run finished
[step3] registered credit_risk version=1
[step3] alias Production -> v1
{
  "run_id": "7dc5896e2fb744608004bdab8ad505e7",
  "version": "1",
  "accuracy": 0.8475,
  "roc_auc": 0.4971981237003723,
  "model_uri": "models:/credit_risk@Production",
  "artifact_uri": "s3://mlflow-artifacts/1/7dc5896e2fb744608004bdab8ad505e7/artifacts/model"
}