Follow these sample notebooks to learn:

1. Logging API: experiment with various logging functions to create runs and automatically generate graphs.
2. Manage runs: learn different ways how to start runs and child runs, monitor them, and cancel them.
3. Tensorboard to monitor runs

Use MLflow with Azure Machine Learning service (Preview)

MLflow is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace.

Try out the sample notebooks:

1. Use MLflow with Azure Machine Learning for Local Training Run
2. Use MLflow with Azure Machine Learning for Remote Training Run
3. Use MLflow with Azure Machine Learning to submit runs locally with MLflow projects
4. Use MLflow with Azure Machine Learning to submit runs on AzureML compute with MLflow projects