Problem loading a pyfunc model in job run

[AmineM]

Hi, I'm currently working on a automated job to predict forecasts using a notebook than work just fine when I run it manually, but keep failling when schedueled, here is my code: 

import mlflow

# Load model as a PyFuncModel.
loaded_model = mlflow.pyfunc.load_model(logged_model)
# Predict using the model
results_df = loaded_model.predict(predict_df)

# Define group_column and time_column
group_column = "id"  # Replace with your actual group column name
time_column = "week_date_format"    # Replace with your actual time column name
target_column = "sales_value"

# Display the prediction results with timestamp for each id
final_df = results_df.reset_index()[[group_column, time_column, "yhat"]].tail(
    forecast_horizon * predict_df[group_column].nunique()
)
final_df = final_df.rename(columns={'yhat': target_column})
display(final_df)

The other cells where mflow is installed and model dependecies are working fyi. 
PS: I use serverless job compute. 

[AmineM]

and here is the error that I get: 

TypeError: code() argument 13 must be str, not int
File <command-32974490616971>, line 16
     13 logged_model = 'runs:/f715739d09624676b443cb02e7c98cc0/model'
     15 # Load model as a PyFuncModel.
---> 16 loaded_model = mlflow.pyfunc.load_model(logged_model)
     17 # Predict using the model
     18 results_df = loaded_model.predict(predict_df)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-222c73bc-a540-4c26-aa9b-af028baf9eca/lib/python3.11/site-packages/mlflow/pyfunc/model.py:659, in _load_context_model_and_signature(model_path, model_config)
    657         raise MlflowException("Python model path was not specified in the model configuration")
    658     with open(os.path.join(model_path, python_model_subpath), "rb") as f:
--> 659         python_model = cloudpickle.load(f)
    661 artifacts = {}
    662 for saved_artifact_name, saved_artifact_info in pyfunc_config.get(
    663     CONFIG_KEY_ARTIFACTS, {}
    664 ).items():

[AmineM]

found a momentary solution : use a serving endpoint but it increase costs