Convert custom ML models to MLflow formatted models

In this article, learn how to convert your custom ML model into MLflow format. MLflow is an open-source library for managing the lifecycle of your machine learning experiments. In some cases, you might use a machine learning framework without its built-in MLflow model flavor support. Due to this lack of built-in MLflow model flavor, you cannot log or register the model with MLflow model fluent APIs. To resolve this, you can convert your model to an MLflow format where you can leverage the following benefits of Azure Machine Learning and MLflow models.

With Azure Machine Learning, MLflow models get the added benefits of,

  • No code deployment
  • Portability as an open source standard format
  • Ability to deploy both locally and on cloud

MLflow provides support for a variety of machine learning frameworks (scikit-learn, Keras, Pytorch, and more); however, it might not cover every use case. For example, you may want to create an MLflow model with a framework that MLflow does not natively support or you may want to change the way your model does pre-processing or post-processing when running jobs. To know more about MLflow models read From artifacts to models in MLflow.

If you didn't train your model with MLFlow and want to use Azure Machine Learning's MLflow no-code deployment offering, you need to convert your custom model to MLFLow. Learn more about custom Python models and MLflow.


Only the mlflow package installed is needed to convert your custom models to an MLflow format.

Create a Python wrapper for your model

Before you can convert your model to an MLflow supported format, you need to first create a Python wrapper for your model. The following code demonstrates how to create a Python wrapper for an sklearn model.

# Load training and test datasets
from sys import version_info
import sklearn
import mlflow.pyfunc

PYTHON_VERSION = "{major}.{minor}.{micro}".format(major=version_info.major,

# Train and save an SKLearn model
sklearn_model_path = "model.pkl"

artifacts = {
    "sklearn_model": sklearn_model_path

# create wrapper
class SKLearnWrapper(mlflow.pyfunc.PythonModel):

    def load_context(self, context):
        import pickle
        self.sklearn_model = pickle.load(open(context.artifacts["sklearn_model"], 'rb'))
    def predict(self, model, data):
        return self.sklearn_model.predict(data)

Create a Conda environment

Next, you need to create Conda environment for the new MLflow Model that contains all necessary dependencies. If not indicated, the environment is inferred from the current installation. If not, it can be specified.

import cloudpickle
conda_env = {
    'channels': ['defaults'],
    'dependencies': [
        'pip': [
    'name': 'sklearn_env'

Load the MLFlow formatted model and test predictions

Once your environment is ready, you can pass the SKlearnWrapper, the Conda environment, and your newly created artifacts dictionary to the mlflow.pyfunc.save_model() method. Doing so saves the model to your disk.

mlflow_pyfunc_model_path = "sklearn_mlflow_pyfunc_custom"
mlflow.pyfunc.save_model(path=mlflow_pyfunc_model_path, python_model=SKLearnWrapper(), conda_env=conda_env, artifacts=artifacts)

To ensure your newly saved MLflow formatted model didn't change during the save, you can load your model and print out a test prediction to compare your original model.

The following code prints a test prediction from the mlflow formatted model and a test prediction from the sklearn model that's saved to your disk for comparison.

loaded_model = mlflow.pyfunc.load_model(mlflow_pyfunc_model_path)

input_data = "<insert test data>"
# Evaluate the model
import pandas as pd
test_predictions = loaded_model.predict(input_data)

# load the model from disk
import pickle
loaded_model = pickle.load(open(sklearn_model_path, 'rb'))
result = loaded_model.predict(input_data)

Register the MLflow formatted model

Once you've confirmed that your model saved correctly, you can create a test run, so you can register and save your MLflow formatted model to your model registry.




In some cases, you might use a machine learning framework without its built-in MLflow model flavor support. For instance, the vaderSentiment library is a standard natural language processing (NLP) library used for sentiment analysis. Since it lacks a built-in MLflow model flavor, you cannot log or register the model with MLflow model fluent APIs. See an example on how to save, log and register a model that doesn't have a supported built-in MLflow model flavor.

Next steps