MLflow Model Registry

Managing MLFlow logged models and artifacts

MLflow is a popular tool that helps you track experiments, manage models and even deploy them to different environments. ZenML already provides a MLflow Experiment Tracker that you can use to track your experiments, and an MLflow Model Deployer that you can use to deploy your models locally.

The MLflow model registry uses the MLflow model registry service to manage and track ML models and their artifacts and provides a user interface to browse them:

When would you want to use it?

You can use the MLflow model registry throughout your experimentation, QA, and production phases to manage and track machine learning model versions. It is designed to help teams collaborate on model development and deployment, and keep track of which models are being used in which environments. With the MLflow model registry, you can store and manage models, deploy them to different environments, and track their performance over time.

This is particularly useful in the following scenarios:

If you are working on a machine learning project and want to keep track of different model versions as they are developed and deployed.

If you need to deploy machine learning models to different environments and want to keep track of which version is being used in each environment.

If you want to monitor and compare the performance of different model versions over time and make data-driven decisions about which models to use in production.

If you want to simplify the process of deploying models either to a production environment or to a staging environment for testing.

How do you deploy it?

The MLflow Experiment Tracker flavor is provided by the MLflow ZenML integration, so you need to install it on your local machine to be able to register an MLflow model registry component. Note that the MLFlow model registry requires MLFlow Experiment Tracker to be present in the stack.

zenml integration install mlflow -y