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[!div class="op_single_selector" title1="Select the version of the Azure Machine Learning developer platform that you're using:"]
MLflow is an open-source framework that's designed to manage the complete machine learning lifecycle. Its ability to train and serve models on different platforms allows you to use a consistent set of tools regardless of where your experiments are running: locally on your computer, on a remote compute target, on a virtual machine, or on an Azure Machine Learning compute instance.
Tip
Azure Machine Learning workspaces are MLflow-compatible, which means you can use Azure Machine Learning workspaces in the same way that you use an MLflow tracking server. Such compatibility has the following advantages:
- We don't host MLflow server instances under the hood. The workspace can talk the MLflow standard.
- You can use Azure Machine Learning workspaces as your tracking server for any MLflow code, whether it runs on Azure Machine Learning or not. You only need to configure MLflow to point to the workspace where the tracking should happen.
- You can run any training routine that uses MLflow in Azure Machine Learning without any change.
Note
Unlike the Azure Machine Learning SDK v1, there's no logging functionality in the SDK v2 and we recommend using MLflow for logging. Such strategy allows your training routines to become cloud-agnostic and portable, removing any dependency in your code with Azure Machine Learning.
Azure Machine Learning uses MLflow Tracking for metric logging and artifact storage for your experiments. When connected to Azure Machine Learning, all tracking performed using MLflow is materialized in the workspace you are working on. To learn more about how to instrument your experiments for tracking experiments and training routines, see Log metrics, parameters, and files with MLflow. You can also use MLflow to Query & compare experiments and runs with MLflow.
You can connect MLflow to Azure Machine Learning workspaces even when you are running locally or in a different cloud. The workspace provides a centralized, secure, and scalable location to store training metrics and models.
Capabilities include:
- Track machine learning experiments and models running locally or in the cloud with MLflow in Azure Machine Learning.
- Track Azure Databricks machine learning experiments with MLflow in Azure Machine Learning.
- Track Azure Synapse Analytics machine learning experiments with MLflow in Azure Machine Learning.
- Training and tracking an XGBoost classifier with MLflow: Demonstrates how to track experiments by using MLflow, log models, and combine multiple flavors into pipelines.
- Training and tracking an XGBoost classifier with MLflow using service principal authentication: Demonstrates how to track experiments by using MLflow from compute that's running outside Azure Machine Learning. It shows how to authenticate against Azure Machine Learning services by using a service principal.
- Hyper-parameter optimization using Hyperopt and nested runs in MLflow: Demonstrates how to use child runs in MLflow to do hyper-parameter optimization for models by using the popular library Hyperopt. It shows how to transfer metrics, parameters, and artifacts from child runs to parent runs.
- Logging models with MLflow: Demonstrates how to use the concept of models instead of artifacts with MLflow, including how to construct custom models.
- Manage runs and experiments with MLflow: Demonstrates how to query experiments, runs, metrics, parameters, and artifacts from Azure Machine Learning by using MLflow.
Important
- MLflow in R support is limited to tracking experiment's metrics, parameters and models on Azure Machine Learning jobs. Interactive training on RStudio, Posit (formerly RStudio Workbench) or Jupyter Notebooks with R kernels is not supported. Model management and registration is not supported using the MLflow R SDK. As an alternative, use Azure ML CLI or Azure ML studio for model registration and management. View the following R example about using the MLflow tracking client with Azure Machine Learning.
- MLflow in Java support is limited to tracking experiment's metrics and parameters on Azure Machine Learning jobs. Artifacts and models can't be tracked using the MLflow Java SDK. As an alternative, use the
Outputsfolder in jobs along with the methodmlflow.save_modelto save models (or artifacts) you want to capture. View the following Java example about using the MLflow tracking client with the Azure Machine Learning.
Azure Machine Learning supports MLflow for model management. This support represents a convenient way to support the entire model lifecycle for users who are familiar with the MLflow client.
To learn more about how to manage models by using the MLflow API in Azure Machine Learning, view Manage model registries in Azure Machine Learning with MLflow.
- Manage model registries with MLflow: Demonstrates how to manage models in registries by using MLflow.
You can deploy MLflow models to Azure Machine Learning and take advantage of the improved experience when you use this type of models. Azure Machine Learning supports deploying MLflow models to both real-time and batch endpoints without having to indicate and environment or a scoring script. Deployment is supported using either MLflow SDK, Azure Machine Learning CLI, Azure Machine Learning SDK for Python, or the Azure Machine Learning studio portal.
Learn more at Guidelines for deploying MLflow models.
- Deploy MLflow to Online Endpoints: Demonstrates how to deploy models in MLflow format to online endpoints using MLflow SDK.
- Deploy MLflow to Online Endpoints with safe rollout: Demonstrates how to deploy models in MLflow format to online endpoints using MLflow SDK with progressive rollout of models and the deployment of multiple model's versions in the same endpoint.
- Deploy MLflow to web services (V1): Demonstrates how to deploy models in MLflow format to web services (ACI/AKS v1) using MLflow SDK.
- Deploying models trained in Azure Databricks to Azure Machine Learning with MLflow: Demonstrates how to train models in Azure Databricks and deploy them in Azure ML. It also includes how to handle cases where you also want to track the experiments with the MLflow instance in Azure Databricks.
You can submit training jobs to Azure Machine Learning by using MLflow projects (preview). You can submit jobs locally with Azure Machine Learning tracking or migrate your jobs to the cloud via Azure Machine Learning compute.
Learn more at Train machine learning models with MLflow projects and Azure Machine Learning.
- Track an MLflow project in Azure Machine Learning workspaces
- Train and run an MLflow project on Azure Machine Learning jobs.
The following table shows which operations are supported by each of the tools available in the machine learning lifecycle.
| Feature | MLflow SDK | Azure Machine Learning CLI/SDK | Azure Machine Learning studio |
|---|---|---|---|
| Track and log metrics, parameters, and models | ✓ | ||
| Retrieve metrics, parameters, and models | ✓ | 1 | ✓ |
| Submit training jobs | ✓ 2 | ✓ | ✓ |
| Submit training jobs with Azure Machine learning data assets | ✓ | ✓ | |
| Submit training jobs with machine learning pipelines | ✓ | ✓ | |
| Manage experiments and runs | ✓ | ✓ | ✓ |
| Manage MLflow models | ✓3 | ✓ | ✓ |
| Manage non-MLflow models | ✓ | ✓ | |
| Deploy MLflow models to Azure Machine Learning (Online & Batch) | ✓4 | ✓ | ✓ |
| Deploy non-MLflow models to Azure Machine Learning | ✓ | ✓ |
Note
- 1 Only artifacts and models can be downloaded.
- 2 Using MLflow projects (preview).
- 3 Some operations may not be supported. View Manage model registries in Azure Machine Learning with MLflow for details.
- 4 Deployment of MLflow models to batch inference by using the MLflow SDK is not possible at the moment. As an alternative, see Deploy and run MLflow models in Spark jobs.