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MLflow and Azure Machine Learning (v1)

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APPLIES TO: Azure CLI ml extension v1 Python SDK azureml v1

MLflow is an open-source library for managing the life cycle of your machine learning experiments. MLflow's tracking URI and logging API are collectively known as MLflow Tracking. This component of MLflow logs and tracks your training run metrics and model artifacts, no matter where your experiment's environment is--on your computer, on a remote compute target, on a virtual machine, or in an Azure Databricks cluster.

Together, MLflow Tracking and Azure Machine Learning allow you to track an experiment's run metrics and store model artifacts in your Azure Machine Learning workspace.

Compare MLflow and Azure Machine Learning clients

The following table summarizes the clients that can use Azure Machine Learning and their respective capabilities.

MLflow Tracking offers metric logging and artifact storage functionalities that are otherwise available only through the Azure Machine Learning Python SDK.

Capability MLflow Tracking and deployment Azure Machine Learning Python SDK Azure Machine Learning CLI Azure Machine Learning studio
Manage a workspace ✓ ✓ ✓
Use data stores ✓ ✓
Log metrics ✓ ✓
Upload artifacts ✓ ✓
View metrics ✓ ✓ ✓ ✓
Manage compute ✓ ✓ ✓
Deploy models ✓ ✓ ✓ ✓
Monitor model performance ✓
Detect data drift ✓ ✓

Track experiments

With MLflow Tracking, you can connect Azure Machine Learning as the back end of your MLflow experiments. You can then do the following tasks:

Train MLflow projects (preview)

Important

Items marked (preview) in this article are currently in public preview. The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

You can use MLflow Tracking to submit training jobs with MLflow Projects and Azure Machine Learning back-end support.

You can submit jobs locally with Azure Machine Learning tracking or migrate your runs to the cloud via Azure Machine Learning compute.

Learn more at Train machine learning models with MLflow projects and Azure Machine Learning.

Deploy MLflow experiments

You can deploy your MLflow model as an Azure web service so that you can apply the model management and data drift detection capabilities in Azure Machine Learning to your production models.

Next steps