MLflow Models

An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, real-time serving through a REST API or batch inference on Apache Spark. They provide a convention to save a model in different “flavors” that can be understood by different downstream tools.

Storage Format

Each MLflow Model is simply a directory containing arbitrary files, together with an MLmodel file in the root of the directory that can define multiple flavors that the model can be viewed in.

Flavors are the key concept that makes MLflow Models powerful: they are a convention that deployment tools can use to understand the model, which makes it possible to write tools that work with models from any ML library without having to integrate each tool with each library. MLflow defines several “standard” flavors that all of its built-in deployment tools support, such as a “Python function” flavor that describes how to run the model as a Python function. However, libraries can also define and use other flavors. For example, MLflow’s mlflow.sklearn library allows loading models back as a scikit-learn Pipeline object for use in code that is aware of scikit-learn, or as a generic Python function for use in tools that just need to apply the model (for example, the mlflow sagemaker tool for deploying models to Amazon SageMaker).

All of the flavors that a particular model supports are defined in its MLmodel file in YAML format. For example, mlflow.sklearn outputs models as follows:

# Directory written by mlflow.sklearn.save_model(model, "my_model")
├── MLmodel
└── model.pkl

And its MLmodel file describes two flavors:

time_created: 2018-05-25T17:28:53.35

    sklearn_version: 0.19.1
    pickled_model: model.pkl
    loader_module: mlflow.sklearn

This model can then be used with any tool that supports either the sklearn or python_function model flavor. For example, the mlflow sklearn command can serve a model with the sklearn flavor:

mlflow sklearn serve my_model

In addition, the mlflow sagemaker command-line tool can package and deploy models to AWS SageMaker as long as they support the python_function flavor:

mlflow sagemaker deploy -m my_model [other options]

Fields in the MLmodel Format

Apart from a flavors field listing the model flavors, the MLmodel YAML format can contain the following fields:

Date and time when the model was created, in UTC ISO 8601 format.
ID of the run that created the model, if the model was saved using MLflow Tracking.

Model API

You can save and load MLflow Models in multiple ways. First, MLflow includes integrations with several common libraries. For example, mlflow.sklearn contains save_model, log_model and load_model functions for Scikit-learn models. Second, you can use the more general mlflow.models.Model class to create and write models. This class has four key functions:

  • add_flavor to add a flavor to the model. Each flavor has a string name and a dictionary of key-value attributes, where the values can be any object that can be serialized to YAML.
  • save saves the model to a local directory.
  • log_artifact logs the model as an artifact in the current run using MLflow Tracking.
  • Model.load loads a model from a local directory or from an artifact in a previous run.

Built-In Model Flavors

MLflow provides several standard flavors that might be useful in your applications. Specifically, many of its deployment tools support these flavors, so you can export your own model in one of these flavors to benefit from all these tools.

Python Function (python_function)

The Python Function flavor defines a generic filesystem format for Python models and provides utilities for saving and loading models to and from this format. The format is self-contained in the sense that it includes all the information necessary to load and use a model. Dependencies are stored either directly with the model or referenced via Conda environment.

The convention for Pyfunc models is to have a predict method or function with the following signature:

predict(data: pandas.DataFrame) -> pandas.DataFrame | numpy.array

Other MLflow components expect Pyfunc models to follow this convention.

The Pyfunc model format is defined as a directory structure containing all required data, code and configuration:

    ./MLmodel - config
    <code> - any code packaged with the model (specified in the conf file, see below)
    <data> - any data packaged with the model (specified in the conf file, see below)
    <env>  - conda environment definition (specified in the conf file, see below)

A Pyfunc model directory must contain an MLmodel file in its root with “python_function” format and the following parameters:

- loader_module [required]:
      Python module that can load the model. Expected to be a module identifier
      (e.g. ``mlflow.sklearn``) importable via ``importlib.import_module``.
      The imported module must contain a function with the following signature:

           load_pyfunc(path: string) -> <pyfunc model>

      The path argument is specified by the data parameter and may refer to a file or directory.

- code [optional]:
      A relative path to a directory containing the code packaged with this model.
      All files and directories inside this directory are added to the Python path
      prior to importing the model loader.

- data [optional]:
      A relative path to a file or directory containing model data.
      the path is passed to the model loader.

- env [optional]:
      A relative path to an exported Conda environment. If present this environment
      will be activated prior to running the model.


>tree example/sklearn_iris/mlruns/run1/outputs/linear-lr
├── MLmodel
├── code
│   ├──
├── data
│   └── model.pkl
└── mlflow_env.yml

>cat example/sklearn_iris/mlruns/run1/outputs/linear-lr/MLmodel
  code: code
  data: data/model.pkl
  env: mlflow_env.yml
  main: sklearn_iris

For more detail see docs at mlflow.pyfunc

Scikit-learn (sklearn)

The sklearn model flavor provides an easy to use interface for handling scikit-learn models with no external dependencies. It saves and loads models using Python’s pickle module and also generates a valid Python Function flavor. For more information, see mlflow.sklearn.

TensorFlow (tensorflow)

The TensorFlow model flavor enables logging TensorFlow Saved Models and loading them back as Python Function models for inference on Pandas DataFrames. Given a directory containing a saved model, you can log the model to MLflow via log_saved_model. The saved model can then be loaded for inference via load_pyfunc(). For more information, see mlflow.tensorflow.

Custom Flavors

In general, you can add any flavor you’d like in MLmodel files, either by writing them directly or building them with the mlflow.models.Model class. Just choose an arbitrary string name for your flavor. MLflow’s tools will ignore flavors that they do not understand in the MLmodel file.

Built-In Deployment Tools

MLflow provides tools for deployment on a local machine and several production environments. You can use these tools to easily apply your models in a production environment. Not all deployment methods are available for all model flavors. Deployment is currently supported mostly for the python function format and all compatible formats.


MLflow can deploy models locally as a local REST API endpoint or to directly score csv files. This functionality is a convenient way of testing models before uploading to remote.

Python function flavor can be deployed locally via mlflow.pyfunc module as

  • serve deploys model as a local REST api server
  • predict uses the model to generate prediction for local csv file.

For more info, see

mlflow pyfunc --help
mlflow pyfunc serve --help
mlflow pyfunc predict --help

Microsoft AzureML

MLflow’s mlflow.azureml module can export Python Function models as Azure ML compatible models. It can also be used to directly deploy and serve models on Azure ML, provided the environment has been correctly set up.

  • export exports the model in Azure ML-compatible format. MLFlow will output a directory with the dependencies necessary to deploy the model.
  • deploy deploys the model directly to Azure ML. You first need to set up your environment to work with the Azure ML CLI. Currently this can be done by starting a shell from the Azure ML Workbench application. You also have to set up all accounts required to run and deploy on Azure ML. Note that where the model is deployed is dependent on your active Azure ML environment. If the active environment is set up for local deployment, the model will be deployed locally in a Docker container (Docker is required).

Model export example:

mlflow azureml export -m <path-to-model> -o test-output
tree test-output
├──  - you can use this script to upload the model to Azure ML
├── - main module required by Azure ML
└── test-output - dir containing MLFlow model in Python Function flavor

Example model worklow for deployment:

az ml set env <local-env> - set environment to local deployment
mlflow azureml deploy <parameters> - deploy locally to test the model
az ml set env <cluster-env> - set environment to cluster
mlflow azureml deploy <parameters> - deploy to the cloud

For more info, see

mlflow azureml --help
mlflow azureml export --help
mlflow azureml deploy --help

Amazon Sagemaker

MLflow’s mlflow.sagemaker module can deploy Python Function models on Sagemaker or locally in a docker container with Sagemaker compatible environment (Docker is required). Similarly to Azure ML, you have to set up your environment and user accounts first in order to deploy to Sagemaker with MLflow. Also, in order to export a custom model to Sagemaker, you need a MLflow-compatible Docker image to be available on Amazon ECR. MLflow provides a default Docker image defintion, however, it is up to the user to build the actual image and upload it to ECR. MLflow includes a utility function to perform this step. Once built and uploaded, the MLflow container can be used for all MLflow models.

  • build-and-push-container builds an MLFLow Docker image and uploads it to ECR. The calling user has to have the correct permissions set up. The image is built locally and requires Docker to be present on the machine that performs this step.
  • run_local deploys the model locally in a Docker container. The image and the environment should be identical to how the model would be run remotely and it is therefore useful for testing the model prior to deployment.
  • deploy deploys the model on Amazon Sagemaker. MLflow will upload the Python Function model into S3 and start an Amazon Sagemaker endpoint serving the model.

Example workflow:

mlflow sagemaker build-and-push-container  - build the container (only needs to be called once)
mlflow sagemaker run-local -m <path-to-yourmodel>  - test the model locally
mlflow sagemaker deploy <parameters> - deploy the model to the cloud

For more info, see

mlflow sagemaker --help
mlflow sagemaker build-and-push-container --help
mlflow sagemaker run-local --help
mlflow sagemaker deploy --help


MLFLow can output python function model as a Spark UDF, which can be uploaded to a Spark cluster and used to score the model.


pyfunc_udf = mlflow.pyfunc.spark_udf(<path-to-model>)
df = spark_df.withColumn("prediction", pyfunc_udf(<features>))