How to change Sklearn flavors version in mlflow on azure machine learning?

Question

I need to change the flavors "sklearn_version" in mlflow from "0.22.1" to "1.0.0" on azure machine learning when I log my trained model, since this model will be incompatible with the sklearn version that I am using for deployment during inference. I could change the version of conda by setting "conda_env" in

   mlflow.sklearn.log_model(conda_env= 'my_env')  

in the conda.yaml file, however it still remains unchanged in flavors in MLmodel file

and here is script that I use to create this mlflow experiment in azure machine learning notebooks.

import mlflow  
from sklearn.tree import DecisionTreeRegressor  
  
from azureml.core import Workspace  
from azureml.core.model import Model  
from azureml.mlflow import register_model  
  
  
def run_model(ws, experiment_name, run_name, x_train, y_train):  
      
    # set up MLflow to track the metrics  
    mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())  
    mlflow.set_experiment(experiment_name)    
      
    with mlflow.start_run(run_name=run_name) as run:  
          
        # fit model  
        regression_model = DecisionTreeRegressor()  
        regression_model.fit(x_train, y_train)  
	  
	    # log training score   
        training_score = regression_model.score(x_train, y_train)  
        mlflow.log_metric("Training score", training_score)  
  
        my_conda_env = {  
                    "name": "mlflow-env",  
                    "channels": ["conda-forge"],  
                    "dependencies": [  
                        "python=3.8.5",  
                        {  
                            "pip": [  
                                "pip",  
                                "scikit-learn~=1.0.0",  
                                "uuid==1.30",  
                                "lz4==4.0.0",  
                                "psutil==5.9.0",  
                                "cloudpickle==1.6.0",  
                                "mlflow",  
                            ],  
                        },  
                    ],  
                }  
  
          
        # register the model  
        mlflow.sklearn.log_model(regression_model, "model", conda_env=my_conda_env)  
  
    model_uri = f"runs:/{run.info.run_id}/model"  
    model = mlflow.register_model(model_uri, "sklearn_regression_model")  
  
if __name__ == '__main__':  
  
    # connect to your workspace  
    ws = Workspace.from_config()  
  
    # create experiment and start logging to a new run in the experiment  
    experiment_name = "exp_name"  
  
    # mlflow run name  
    run_name= '1234'  
  
    
    # get train data  
    x_train, y_train  = get_train_data()  
      
    run_model(ws, experiment_name, run_name, x_train, y_train)  
  

Any idea how can change the flavor sklearn version in "MLmodel" file in my script?

With many thanks in advance!

Answer 1

Thanks for your response!! I was able to solve this issue by updating scikit-learn within my workspace. Mlflow MLmodel takes that version of scikit-learn to generate flavors. But I think your solution is also correct.

Answer 2

@Anonymous Thanks for the question. Which version of Azure ML SDK are you using?. Here is the sample that could help to custom MLmodel.