MLflow

Deploying your models locally with MLflow.

The MLflow Model Deployer is one of the available flavors of the Model Deployer stack component. Provided with the MLflow integration it can be used to deploy and manage MLflow models on a local running MLflow server.

The MLflow Model Deployer is not yet available for use in production. This is a work in progress and will be available soon. At the moment it is only available for use in a local development environment.

When to use it?

MLflow is a popular open-source platform for machine learning. It's a great tool for managing the entire lifecycle of your machine learning. One of the most important features of MLflow is the ability to package your model and its dependencies into a single artifact that can be deployed to a variety of deployment targets.

You should use the MLflow Model Deployer:

if you want to have an easy way to deploy your models locally and perform real-time predictions using the running MLflow prediction server.

if you are looking to deploy your models in a simple way without the need for a dedicated deployment environment like Kubernetes or advanced infrastructure configuration.

If you are looking to deploy your models in a more complex way, you should use one of the other Model Deployer Flavors available in ZenML.

How do you deploy it?

The MLflow Model Deployer flavor is provided by the MLflow ZenML integration, so you need to install it on your local machine to be able to deploy your models. You can do this by running the following command:

zenml integration install mlflow -y

To register the MLflow model deployer with ZenML you need to run the following command:

zenml model-deployer register mlflow_deployer --flavor=mlflow

The ZenML integration will provision a local MLflow deployment server as a daemon process that will continue to run in the background to serve the latest MLflow model.

How do you use it?

Deploy a logged model