| title | titleSuffix | description | services | author | ms.author | ms.service | ms.subservice | ms.date | ms.topic | ms.custom |
|---|---|---|---|---|---|---|---|---|---|---|
MLflow and Azure Machine Learning |
Azure Machine Learning |
Learn about how Azure Machine Learning uses MLflow to log metrics and artifacts from ML models, and deploy your ML models to an endpoint. |
machine-learning |
abeomor |
osomorog |
machine-learning |
mlops |
04/15/2022 |
conceptual |
devx-track-python, cliv2, sdkv2, event-tier1-build-2022 |
[!INCLUDE dev v2]
[!div class="op_single_selector" title1="Select the version of Azure Machine Learning developer platform you are using:"]
Azure Machine Learning only uses MLflow Tracking for metric logging and artifact storage for your experiments, whether you created the experiment via the Azure Machine Learning Python SDK, Azure Machine Learning CLI or the Azure Machine Learning studio.
Note
Unlike the Azure Machine Learning SDK v1, there is no logging functionality in the SDK v2 (preview), and it is recommended to use MLflow for logging and tracking.
MLflow is an open-source library for managing the lifecycle of your machine learning experiments. MLflow's tracking URI and logging API, collectively known as MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine or an Azure Machine Learning compute instance.
With MLflow Tracking you can connect Azure Machine Learning as the backend of your MLflow experiments. By doing so, you can
-
Track and log experiment metrics and artifacts in your Azure Machine Learning workspace. If you already use MLflow Tracking for your experiments, the workspace provides a centralized, secure, and scalable location to store training metrics and models. Learn more at Track ML models with MLflow and Azure Machine Learning CLI v2.
-
Model management in MLflow or Azure Machine Learning model registry.
You can Deploy MLflow models to an online endpoint, so you can leverage and apply Azure Machine Learning's model management capabilities and no-code deployment offering.
[!INCLUDE preview disclaimer]
You can use MLflow's tracking URI and logging API, collectively known as MLflow Tracking, to submit training jobs with MLflow Projects and Azure Machine Learning backend support (preview). You can submit jobs locally with Azure Machine Learning tracking or migrate your runs to the cloud like via an Azure Machine Learning Compute.
Learn more at Train ML models with MLflow projects and Azure Machine Learning (preview).