MLflow

Logging and visualizing experiments with MLflow.

The MLflow Experiment Tracker is an Experiment Tracker flavor provided with the MLflow ZenML integration that uses the MLflow tracking service to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).

When would you want to use it?

MLflow Tracking is a very popular tool that you would normally use in the iterative ML experimentation phase to track and visualize experiment results. That doesn't mean that it cannot be repurposed to track and visualize the results produced by your automated pipeline runs, as you make the transition toward a more production-oriented workflow.

You should use the MLflow Experiment Tracker:

if you have already been using MLflow to track experiment results for your project and would like to continue doing so as you are incorporating MLOps workflows and best practices in your project through ZenML.

if you are looking for a more visually interactive way of navigating the results produced from your ZenML pipeline runs (e.g. models, metrics, datasets)

if you or your team already have a shared MLflow Tracking service deployed somewhere on-premise or in the cloud, and you would like to connect ZenML to it to share the artifacts and metrics logged by your pipelines

You should consider one of the other Experiment Tracker flavors if you have never worked with MLflow before and would rather use another experiment tracking tool that you are more familiar with.

How do you deploy it?

The MLflow Experiment Tracker flavor is provided by the MLflow ZenML integration, you need to install it on your local machine to be able to register an MLflow Experiment Tracker and add it to your stack:

zenml integration install mlflow -y

The MLflow Experiment Tracker can be configured to accommodate the following MLflow deployment scenarios: