1  # This is an example for logging a Python model from code using the
 2  # mlflow.pyfunc.log_model API. When a path to a valid Python script is submitted to the
 3  # python_model argument, the model code itself is serialized instead of the model object.
 4  # Within the targeted script, the model implementation must be defined and set by
 5  # using the mlflow.models.set_model API.
 6  
 7  import pandas as pd
 8  
 9  import mlflow
10  
11  input_example = ["What is the weather like today?"]
12  
13  # Specify the path to the model notebook
14  model_path = "model_as_code.py"
15  print(f"Model path: {model_path}")
16  
17  print("Logging model as code using Pyfunc log model API")
18  with mlflow.start_run():
19      model_info = mlflow.pyfunc.log_model(
20          python_model=model_path,
21          name="ai-model",
22          input_example=input_example,
23      )
24  
25  print("Loading model using Pyfunc load model API")
26  pyfunc_model = mlflow.pyfunc.load_model(model_info.model_uri)
27  output = pyfunc_model.predict(pd.DataFrame(input_example, columns=["input"]))
28  print(f"Output: {output}")