In this entry point tutorial to MLflow, we’ll be covering the essential basics of core MLflow functionality associated with tracking training event data.
We’ll start by learning how to start a local MLflow Tracking server, how to access and view the MLflow UI, and move on to our first interactions with the Tracking server through the use of the MLflow Client.
The tutorial content builds upon itself, culminating in successfully logging your first MLflow model.
The topics in this tutorial cover:
Starting an MLflow Tracking Server
Exploring the MlflowClient API (briefly)
Understanding the Default Experiment
Searching for Experiments with the MLflow client API
Understanding the uses of tags and how to leverage them for model organization
Creating an Experiment that will contain our run (and our model)
Learning how to log metrics, parameters, and a model artifact to a run
Viewing our Experiment and our first run within the MLflow UI
To get started with the tutorial, click NEXT below.