Introduction
If you choose to train models using Azure Databricks and track your work using MLflow, you can add an integration with Azure Machine Learning to store model training metrics and artifacts and keep a clear overview of your work. Using Azure Machine Learning as the backend for your MLflow experiments that run on Azure Databricks compute gives you the benefit of having a centralized and scalable workspace where you can access all your assets to run experiments or review them. In this module, you will learn about the integration between all these products and how you can manage your work from the Azure Machine Learning workspace.
Learning objectives
After completing this module, you'll be able to:
- Describe Azure Machine Learning.
- Run an experiment.
- Log metrics with MLflow.
- Run Pipeline Step on Databricks Compute.