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. The format defines a convention that lets you save a model in different “flavors” that can be understood by different downstream tools.

Table of Contents

Storage Format
Model API
Built-In Model Flavors
Model Customization
Built-In Deployment Tools

Storage Format

Each MLflow Model is 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")
my_model/
├── MLmodel
└── model.pkl

And its MLmodel file describes two flavors:

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

flavors:
  sklearn:
    sklearn_version: 0.19.1
    pickled_model: model.pkl
  python_function:
    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 models serve command can serve a model with the sklearn flavor:

mlflow models 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:

time_created: Date and time when the model was created, in UTC ISO 8601 format.
run_id: 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 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 to save the model to a local directory.
log to log the model as an artifact in the current run using MLflow Tracking.
load to load 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)
R Function (crate)
H₂O (h2o)
Keras (keras)
MLeap (mleap)
PyTorch (pytorch)
Scikit-learn (sklearn)
Spark MLlib (spark)
TensorFlow (tensorflow)
ONNX (onnx)
MXNet Gluon (gluon)
XGBoost (xgboost)
LightGBM (lightgbm)

Python Function (`python_function`)

The python_function model 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.

Many MLflow Model persistence modules, such as mlflow.sklearn, mlflow.keras, and mlflow.pytorch, produce models with the python_function (pyfunc) flavor. This means that they adhere to the python_function filesystem format and can be interpreted as generic Python classes that implement the specified inference API. Therefore, any tool that operates on these pyfunc classes can operate on any MLflow Model containing the pyfunc flavor, regardless of which persistence module or framework was used to produce the model. This interoperability is very powerful because it allows any Python model to be productionized in a variety of environments.

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

predict(model_input: pandas.DataFrame) -> [numpy.ndarray | pandas.Series | pandas.DataFrame]

Other MLflow components expect python_function models to follow this convention.

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

The mlflow.pyfunc module defines functions for saving and loading MLflow Models with the python_function flavor. This module also includes utilities for creating custom Python models. For more information, see the custom Python models documentation and the mlflow.pyfunc documentation.

R Function (`crate`)

The crate model flavor defines a generic model format for representing an arbitrary R prediction function as an MLflow model. The prediction function is expected to take a dataframe as input and produce a dataframe, a vector or a list with the predictions as output.

This flavor requires R to be installed in order to be used.

H₂O (`h2o`)

The h2o model flavor enables logging and loading H2O models.

The mlflow.h2o module defines save_model() and log_model() methods for saving H2O models in MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you to load them as generic Python functions for inference via mlflow.pyfunc.load_model(). When you load MLflow Models with the h2o flavor using mlflow.pyfunc.load_model(), the h2o.init() method is called. Therefore, the correct version of h2o(-py) must be installed in the loader’s environment. You can customize the arguments given to h2o.init() by modifying the init entry of the persisted H2O model’s YAML configuration file: model.h2o/h2o.yaml.

Finally, you can use the mlflow.h2o.load_model() method to load MLflow Models with the h2o flavor as H2O model objects.

For more information, see mlflow.h2o.

Keras (`keras`)

The keras model flavor enables logging and loading Keras models. It is available in both Python and R clients. The mlflow.keras module defines save_model() and log_model() functions that you can use to save Keras models in MLflow Model format in Python. Similarly, in R, you can save or log the model using mlflow_save_model and mlflow_log_model. These functions serialize Keras models as HDF5 files using the Keras library’s built-in model persistence functions. MLflow Models produced by these functions also contain the python_function flavor, allowing them to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). Finally, you can use the mlflow.keras.load_model() function in Python or mlflow_load_model function in R to load MLflow Models with the keras flavor as Keras Model objects.

For more information, see mlflow.keras.

MLeap (`mleap`)

The mleap model flavor supports saving Spark models in MLflow format using the MLeap persistence mechanism. MLeap is an inference-optimized format and execution engine for Spark models that does not depend on SparkContext to evaluate inputs.

You can save Spark models in MLflow format with the mleap flavor by specifying the sample_input argument of the mlflow.spark.save_model() or mlflow.spark.log_model() method (recommended). The mlflow.mleap module also defines save_model() and log_model() methods for saving MLeap models in MLflow format, but these methods do not include the python_function flavor in the models they produce.

A companion module for loading MLflow Models with the MLeap flavor is available in the mlflow/java package.

For more information, see mlflow.spark, mlflow.mleap, and the MLeap documentation.

PyTorch (`pytorch`)

The pytorch model flavor enables logging and loading PyTorch models.

The mlflow.pytorch module defines utilities for saving and loading MLflow Models with the pytorch flavor. You can use the mlflow.pytorch.save_model() and mlflow.pytorch.log_model() methods to save PyTorch models in MLflow format; both of these functions use the torch.save() method to serialize PyTorch models. Additionally, you can use the mlflow.pytorch.load_model() method to load MLflow Models with the pytorch flavor as PyTorch model objects. Finally, models produced by mlflow.pytorch.save_model() and mlflow.pytorch.log_model() contain the python_function flavor, allowing you to load them as generic Python functions for inference via mlflow.pyfunc.load_model().

For more information, see mlflow.pytorch.

Scikit-learn (`sklearn`)

The sklearn model flavor provides an easy-to-use interface for saving and loading scikit-learn models. The mlflow.sklearn module defines save_model() and log_model() functions that save scikit-learn models in MLflow format, using either Python’s pickle module (Pickle) or CloudPickle for model serialization. These functions produce MLflow Models with the python_function flavor, allowing them to be loaded as generic Python functions for inference via mlflow.pyfunc.load_model(). Finally, you can use the mlflow.sklearn.load_model() method to load MLflow Models with the sklearn flavor as scikit-learn model objects.

For more information, see mlflow.sklearn.

Spark MLlib (`spark`)

The spark model flavor enables exporting Spark MLlib models as MLflow Models.

The mlflow.spark module defines save_model() and log_model() methods that save Spark MLlib pipelines in MLflow model format. MLflow Models produced by these functions contain the python_function flavor, allowing you to load them as generic Python functions via mlflow.pyfunc.load_model(). When a model with the spark flavor is loaded as a Python function via mlflow.pyfunc.load_model(), a new SparkContext is created for model inference; additionally, the function converts all Pandas DataFrame inputs to Spark DataFrames before scoring. While this initialization overhead and format translation latency is not ideal for high-performance use cases, it enables you to easily deploy any MLlib PipelineModel to any production environment supported by MLflow (SageMaker, AzureML, etc).

Finally, the mlflow.spark.load_model() method is used to load MLflow Models with the spark flavor as Spark MLlib pipelines.

For more information, see mlflow.spark.

TensorFlow (`tensorflow`)

The tensorflow model flavor allows serialized TensorFlow models in SavedModel format to be logged in MLflow format via the mlflow.tensorflow.save_model() and mlflow.tensorflow.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). Finally, you can use the mlflow.tensorflow.load_model() method to load MLflow Models with the tensorflow flavor as TensorFlow graphs.

For more information, see mlflow.tensorflow.

ONNX (`onnx`)

The onnx model flavor enables logging of ONNX models in MLflow format via the mlflow.onnx.save_model() and mlflow.onnx.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). The python_function representation of an MLflow ONNX model uses the ONNX Runtime execution engine for evaluation. Finally, you can use the mlflow.onnx.load_model() method to load MLflow Models with the onnx flavor in native ONNX format.

For more information, see mlflow.onnx and http://onnx.ai/.

MXNet Gluon (`gluon`)

The gluon model flavor enables logging of Gluon models in MLflow format via the mlflow.gluon.save_model() and mlflow.gluon.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.gluon.load_model() method to load MLflow Models with the gluon flavor in native Gluon format.

For more information, see mlflow.gluon.

XGBoost (`xgboost`)

The xgboost model flavor enables logging of XGBoost models in MLflow format via the mlflow.xgboost.save_model() and mlflow.xgboost.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.xgboost.load_model() method to load MLflow Models with the xgboost model flavor in native XGBoost format.

Note that the xgboost model flavor only supports an instance of xgboost.Booster, not models that implement the scikit-learn API.

For more information, see mlflow.xgboost.

LightGBM (`lightgbm`)

The lightgbm model flavor enables logging of LightGBM models in MLflow format via the mlflow.lightgbm.save_model() and mlflow.lightgbm.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.lightgbm.load_model() method to load MLflow Models with the lightgbm model flavor in native LightGBM format.

Note that the lightgbm model flavor only supports an instance of lightgbm.Booster, not models that implement the scikit-learn API.

For more information, see mlflow.lightgbm.

Model Customization

While MLflow’s built-in model persistence utilities are convenient for packaging models from various popular ML libraries in MLflow Model format, they do not cover every use case. For example, you may want to use a model from an ML library that is not explicitly supported by MLflow’s built-in flavors. Alternatively, you may want to package custom inference code and data to create an MLflow Model. Fortunately, MLflow provides two solutions that can be used to accomplish these tasks: Custom Python Models and Custom Flavors.

In this section:

Custom Python Models
- Example: Creating a custom “add n” model
- Example: Saving an XGBoost model in MLflow format
Custom Flavors

Custom Python Models

The mlflow.pyfunc module provides save_model() and log_model() utilities for creating MLflow Models with the python_function flavor that contain user-specified code and artifact (file) dependencies. These artifact dependencies may include serialized models produced by any Python ML library.

Because these custom models contain the python_function flavor, they can be deployed to any of MLflow’s supported production environments, such as SageMaker, AzureML, or local REST endpoints.

The following examples demonstrate how you can use the mlflow.pyfunc module to create custom Python models. For additional information about model customization with MLflow’s python_function utilities, see the python_function custom models documentation.

Example: Creating a custom “add n” model

This example defines a class for a custom model that adds a specified numeric value, n, to all columns of a Pandas DataFrame input. Then, it uses the mlflow.pyfunc APIs to save an instance of this model with n = 5 in MLflow Model format. Finally, it loads the model in python_function format and uses it to evaluate a sample input.

import mlflow.pyfunc

# Define the model class
class AddN(mlflow.pyfunc.PythonModel):

    def __init__(self, n):
        self.n = n

    def predict(self, context, model_input):
        return model_input.apply(lambda column: column + self.n)

# Construct and save the model
model_path = "add_n_model"
add5_model = AddN(n=5)
mlflow.pyfunc.save_model(path=model_path, python_model=add5_model)

# Load the model in `python_function` format
loaded_model = mlflow.pyfunc.load_model(model_path)

# Evaluate the model
import pandas as pd
model_input = pd.DataFrame([range(10)])
model_output = loaded_model.predict(model_input)
assert model_output.equals(pd.DataFrame([range(5, 15)]))

Example: Saving an XGBoost model in MLflow format

This example begins by training and saving a gradient boosted tree model using the XGBoost library. Next, it defines a wrapper class around the XGBoost model that conforms to MLflow’s python_function inference API. Then, it uses the wrapper class and the saved XGBoost model to construct an MLflow Model that performs inference using the gradient boosted tree. Finally, it loads the MLflow Model in python_function format and uses it to evaluate test data.

# Load training and test datasets
import xgboost as xgb
from sklearn import datasets
from sklearn.model_selection import train_test_split

iris = datasets.load_iris()
x = iris.data[:, 2:]
y = iris.target
x_train, x_test, y_train, _ = train_test_split(x, y, test_size=0.2, random_state=42)
dtrain = xgb.DMatrix(x_train, label=y_train)

# Train and save an XGBoost model
xgb_model = xgb.train(params={'max_depth': 10}, dtrain=dtrain, num_boost_round=10)
xgb_model_path = "xgb_model.pth"
xgb_model.save_model(xgb_model_path)

# Create an `artifacts` dictionary that assigns a unique name to the saved XGBoost model file.
# This dictionary will be passed to `mlflow.pyfunc.save_model`, which will copy the model file
# into the new MLflow Model's directory.
artifacts = {
    "xgb_model": xgb_model_path
}

# Define the model class
import mlflow.pyfunc
class XGBWrapper(mlflow.pyfunc.PythonModel):

    def load_context(self, context):
        import xgboost as xgb
        self.xgb_model = xgb.Booster()
        self.xgb_model.load_model(context.artifacts["xgb_model"])

    def predict(self, context, model_input):
        input_matrix = xgb.DMatrix(model_input.values)
        return self.xgb_model.predict(input_matrix)

# Create a Conda environment for the new MLflow Model that contains the XGBoost library
# as a dependency, as well as the required CloudPickle library
import cloudpickle
conda_env = {
    'channels': ['defaults'],
    'dependencies': [
      'xgboost={}'.format(xgb.__version__),
      'cloudpickle={}'.format(cloudpickle.__version__),
    ],
    'name': 'xgb_env'
}

# Save the MLflow Model
mlflow_pyfunc_model_path = "xgb_mlflow_pyfunc"
mlflow.pyfunc.save_model(
        path=mlflow_pyfunc_model_path, python_model=XGBWrapper(), artifacts=artifacts,
        conda_env=conda_env)

# Load the model in `python_function` format
loaded_model = mlflow.pyfunc.load_model(mlflow_pyfunc_model_path)

# Evaluate the model
import pandas as pd
test_predictions = loaded_model.predict(pd.DataFrame(x_test))
print(test_predictions)

Custom Flavors

You can also create custom MLflow Models by writing a custom flavor.

As discussed in the Model API and Storage Format sections, an MLflow Model is defined by a directory of files that contains an MLmodel configuration file. This MLmodel file describes various model attributes, including the flavors in which the model can be interpreted. The MLmodel file contains an entry for each flavor name; each entry is a YAML-formatted collection of flavor-specific attributes.

To create a new flavor to support a custom model, you define the set of flavor-specific attributes to include in the MLmodel configuration file, as well as the code that can interpret the contents of the model directory and the flavor’s attributes.

As an example, let’s examine the mlflow.pytorch module corresponding to MLflow’s pytorch flavor. In the mlflow.pytorch.save_model() method, a PyTorch model is saved to a specified output directory. Additionally, mlflow.pytorch.save_model() leverages the mlflow.models.Model.add_flavor() and mlflow.models.Model.save() functions to produce an MLmodel configuration containing the pytorch flavor. The resulting configuration has several flavor-specific attributes, such as pytorch_version, which denotes the version of the PyTorch library that was used to train the model. To interpret model directories produced by save_model(), the mlflow.pytorch module also defines a load_model() method. mlflow.pytorch.load_model() reads the MLmodel configuration from a specified model directory and uses the configuration attributes of the pytorch flavor to load and return a PyTorch model from its serialized representation.

Built-In Deployment Tools

MLflow provides tools for deploying MLflow models on a local machine and to several production environments. Not all deployment methods are available for all model flavors.

In this section:

Deploy MLflow models
Deploy a python_function model on Microsoft Azure ML
Deploy a python_function model on Amazon SageMaker
Export a python_function model as an Apache Spark UDF

Deploy MLflow models

MLflow can deploy models locally as local REST API endpoints or to directly score files. In addition, MLflow can package models as self-contained Docker images with the REST API endpoint. The image can be used to safely deploy the model to various environments such as Kubernetes.

You deploy MLflow model locally or generate a Docker image using the CLI interface to the mlflow.models module.

The REST API server accepts the following data formats as POST input to the /invocations path:

JSON-serialized pandas DataFrames in the split orientation. For example, data = pandas_df.to_json(orient='split'). This format is specified using a Content-Type request header value of application/json or application/json; format=pandas-split.
JSON-serialized pandas DataFrames in the records orientation. We do not recommend using this format because it is not guaranteed to preserve column ordering. This format is specified using a Content-Type request header value of application/json; format=pandas-records.
CSV-serialized pandas DataFrames. For example, data = pandas_df.to_csv(). This format is specified using a Content-Type request header value of text/csv.

Example requests:

# split-oriented
curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json' -d '{
    "columns": ["a", "b", "c"],
    "data": [[1, 2, 3], [4, 5, 6]]
}'

# record-oriented (fine for vector rows, loses ordering for JSON records)
curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json; format=pandas-records' -d '[
    {"a": 1,"b": 2,"c": 3},
    {"a": 4,"b": 5,"c": 6}
]'

For more information about serializing pandas DataFrames, see pandas.DataFrame.to_json.

The predict command accepts the same input formats. The format is specified as command line arguments.

Commands

serve deploys the model as a local REST API server.
build_docker packages a REST API endpoint serving the model as a docker image.
predict uses the model to generate a prediction for a local CSV or JSON file.

For more info, see:

mlflow models --help
mlflow models serve --help
mlflow models predict --help
mlflow models build-docker --help

Deploy a `python_function` model on Microsoft Azure ML

The mlflow.azureml module can package python_function models into Azure ML container images. These images can be deployed to Azure Kubernetes Service (AKS) and the Azure Container Instances (ACI) platform for real-time serving. The resulting Azure ML ContainerImage contains a web server that accepts the following data formats as input:

JSON-serialized pandas DataFrames in the split orientation. For example, data = pandas_df.to_json(orient='split'). This format is specified using a Content-Type request header value of application/json.
build_image registers an MLflow Model with an existing Azure ML workspace and builds an Azure ML container image for deployment to AKS and ACI. The Azure ML SDK is required in order to use this function. The Azure ML SDK requires Python 3. It cannot be installed with earlier versions of Python.

Example workflow using the Python API

import mlflow.azureml

from azureml.core import Workspace
from azureml.core.webservice import AciWebservice, Webservice


# Create or load an existing Azure ML workspace. You can also load an existing workspace using
# Workspace.get(name="<workspace_name>")
workspace_name = "<Name of your Azure ML workspace>"
subscription_id = "<Your Azure subscription ID>"
resource_group = "<Name of the Azure resource group in which to create Azure ML resources>"
location = "<Name of the Azure location (region) in which to create Azure ML resources>"
azure_workspace = Workspace.create(name=workspace_name,
                                   subscription_id=subscription_id,
                                   resource_group=resource_group,
                                   location=location,
                                   create_resource_group=True,
                                   exist_okay=True)

# Build an Azure ML container image for deployment
azure_image, azure_model = mlflow.azureml.build_image(model_uri="<path-to-model>",
                                                      workspace=azure_workspace,
                                                      description="Wine regression model 1",
                                                      synchronous=True)
# If your image build failed, you can access build logs at the following URI:
print("Access the following URI for build logs: {}".format(azure_image.image_build_log_uri))

# Deploy the container image to ACI
webservice_deployment_config = AciWebservice.deploy_configuration()
webservice = Webservice.deploy_from_image(
                    image=azure_image, workspace=azure_workspace, name="<deployment-name>")
webservice.wait_for_deployment()

# After the image deployment completes, requests can be posted via HTTP to the new ACI
# webservice's scoring URI. The following example posts a sample input from the wine dataset
# used in the MLflow ElasticNet example:
# https://github.com/mlflow/mlflow/tree/master/examples/sklearn_elasticnet_wine
print("Scoring URI is: %s", webservice.scoring_uri)

import requests
import json

# `sample_input` is a JSON-serialized pandas DataFrame with the `split` orientation
sample_input = {
    "columns": [
        "alcohol",
        "chlorides",
        "citric acid",
        "density",
        "fixed acidity",
        "free sulfur dioxide",
        "pH",
        "residual sugar",
        "sulphates",
        "total sulfur dioxide",
        "volatile acidity"
    ],
    "data": [
        [8.8, 0.045, 0.36, 1.001, 7, 45, 3, 20.7, 0.45, 170, 0.27]
    ]
}
response = requests.post(
              url=webservice.scoring_uri, data=json.dumps(sample_input),
              headers={"Content-type": "application/json"})
response_json = json.loads(response.text)
print(response_json)

Example workflow using the MLflow CLI

mlflow azureml build-image -w <workspace-name> -m <model-path> -d "Wine regression model 1"

az ml service create aci -n <deployment-name> --image-id <image-name>:<image-version>

# After the image deployment completes, requests can be posted via HTTP to the new ACI
# webservice's scoring URI. The following example posts a sample input from the wine dataset
# used in the MLflow ElasticNet example:
# https://github.com/mlflow/mlflow/tree/master/examples/sklearn_elasticnet_wine

scoring_uri=$(az ml service show --name <deployment-name> -v | jq -r ".scoringUri")

# `sample_input` is a JSON-serialized pandas DataFrame with the `split` orientation
sample_input='
{
    "columns": [
        "alcohol",
        "chlorides",
        "citric acid",
        "density",
        "fixed acidity",
        "free sulfur dioxide",
        "pH",
        "residual sugar",
        "sulphates",
        "total sulfur dioxide",
        "volatile acidity"
    ],
    "data": [
        [8.8, 0.045, 0.36, 1.001, 7, 45, 3, 20.7, 0.45, 170, 0.27]
    ]
}'

echo $sample_input | curl -s -X POST $scoring_uri\
-H 'Cache-Control: no-cache'\
-H 'Content-Type: application/json'\
-d @-

For more info, see:

mlflow azureml --help
mlflow azureml build-image --help

Deploy a `python_function` model on Amazon SageMaker

The mlflow.sagemaker module can deploy python_function models locally in a Docker container with SageMaker compatible environment and remotely on SageMaker. To deploy remotely to SageMaker you need to set up your environment and user accounts. 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 definition; however, it is up to you to build the image and upload it to ECR. MLflow includes the utility function build_and_push_container to perform this step. Once built and uploaded, you can use the MLflow container for all MLflow Models. Model webservers deployed using the mlflow.sagemaker module accept the following data formats as input, depending on the deployment flavor:

python_function: For this deployment flavor, the endpoint accepts the same formats described in the local model deployment documentation.
mleap: For this deployment flavor, the endpoint accepts only JSON-serialized pandas DataFrames in the split orientation. For example, data = pandas_df.to_json(orient='split'). This format is specified using a Content-Type request header value of application/json.

Commands

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.
build-and-push-container builds an MLfLow Docker image and uploads it to ECR. The caller must have the correct permissions set up. The image is built locally and requires Docker to be present on the machine that performs this step.
deploy deploys the model on Amazon SageMaker. MLflow uploads the Python Function model into S3 and starts an Amazon SageMaker endpoint serving the model.

Example workflow using the MLflow CLI

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

For more info, see:

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

Export a `python_function` model as an Apache Spark UDF

You can output a python_function model as an Apache Spark UDF, which can be uploaded to a Spark cluster and used to score the model.

Example

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

The resulting UDF is based on Spark’s Pandas UDF and is currently limited to producing either a single value or an array of values of the same type per observation. By default, we return the first numeric column as a double. You can control what result is returned by supplying result_type argument. The following values are supported:

'int' or IntegerType: The leftmost integer that can fit in int32 result is returned or exception is raised if there is none.
'long' or LongType: The leftmost long integer that can fit in int64 result is returned or exception is raised if there is none.
ArrayType (IntegerType | LongType): Return all integer columns that can fit into the requested size.
'float' or FloatType: The leftmost numeric result cast to float32 is returned or exception is raised if there is no numeric column.
'double' or DoubleType: The leftmost numeric result cast to double is returned or exception is raised if there is no numeric column.
ArrayType ( FloatType | DoubleType ): Return all numeric columns cast to the requested. type. Exception is raised if there are numeric columns.
'string' or StringType: Result is the leftmost column converted to string.
ArrayType ( StringType ): Return all columns converted to string.

Example

from pyspark.sql.types import ArrayType, FloatType
pyfunc_udf = mlflow.pyfunc.spark_udf(<path-to-model>, result_type=ArrayType(FloatType()))
# The prediction column will contain all the numeric columns returned by the model as floats
df = spark_df.withColumn("prediction", pyfunc_udf(<features>))