What is Azure Machine Learning studio?

In this article, you learn about Azure Machine Learning studio, the web portal for data scientist developers in Azure Machine Learning. The studio combines no-code and code-first experiences for an inclusive data science platform.

In this article you learn:

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Author machine learning projects

The studio offers multiple authoring experiences depending on the type project and the level of user experience.

Screenshot: write and run code in a notebook.

  • Azure Machine Learning designer

    Use the designer to train and deploy machine learning models without writing any code. Drag and drop datasets and components to create ML pipelines. Try out the designer tutorial.

    Azure Machine Learning designer example.

  • Automated machine learning UI

    Learn how to create automated ML experiments with an easy-to-use interface.

    AutoML in the Azure Machine Learning studio navigation pane.

  • Data labeling

    Use Azure Machine Learning data labeling to efficiently coordinate image labeling or text labeling projects.

Manage assets and resources

Manage your machine learning assets directly in your browser. Assets are shared in the same workspace between the SDK and the studio for a seamless experience. Use the studio to manage:

  • Models
  • Datasets
  • Datastores
  • Compute resources
  • Notebooks
  • Experiments
  • Run logs
  • Pipelines
  • Pipeline endpoints

Even if you're an experienced developer, the studio can simplify how you manage workspace resources.

ML Studio (classic) vs Azure Machine Learning studio


Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources (workspace and web service plan). Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) experiments and web services.

ML Studio (classic) documentation is being retired and may not be updated in the future.

Released in 2015, ML Studio (classic) was the first drag-and-drop machine learning model builder in Azure. ML Studio (classic) is a standalone service that only offers a visual experience. Studio (classic) does not interoperate with Azure Machine Learning.

Azure Machine Learning is a separate, and modernized, service that delivers a complete data science platform. It supports both code-first and low-code experiences.

Azure Machine Learning studio is a web portal in Azure Machine Learning that contains low-code and no-code options for project authoring and asset management.

If you're a new user, choose Azure Machine Learning, instead of ML Studio (classic). As a complete ML platform, Azure Machine Learning offers:

  • Scalable compute clusters for large-scale training.
  • Enterprise security and governance.
  • Interoperable with popular open-source tools.
  • End-to-end MLOps.

Feature comparison

The following table summarizes the key differences between ML Studio (classic) and Azure Machine Learning.

Feature ML Studio (classic) Azure Machine Learning
Drag and drop interface Classic experience Updated experience - Azure Machine Learning designer
Code SDKs Not supported Fully integrated with Azure Machine Learning Python and R SDKs
Experiment Scalable (10-GB training data limit) Scale with compute target
Training compute targets Proprietary compute target, CPU support only Wide range of customizable training compute targets. Includes GPU and CPU support
Deployment compute targets Proprietary web service format, not customizable Wide range of customizable deployment compute targets. Includes GPU and CPU support
ML Pipeline Not supported Build flexible, modular pipelines to automate workflows
MLOps Basic model management and deployment; CPU only deployments Entity versioning (model, data, workflows), workflow automation, integration with CICD tooling, CPU and GPU deployments and more
Model format Proprietary format, Studio (classic) only Multiple supported formats depending on training job type
Automated model training and hyperparameter tuning Not supported Supported. Code-first and no-code options.
Data drift detection Not supported Supported
Data labeling projects Not supported Supported
Role-Based Access Control (RBAC) Only contributor and owner role Flexible role definition and RBAC control
AI Gallery Supported (https://gallery.azure.ai/) Unsupported

Learn with sample Python SDK notebooks.


  • Missing user interface items in studio Azure role-based access control can be used to restrict actions that you can perform with Azure Machine Learning. These restrictions can prevent user interface items from appearing in the Azure Machine Learning studio. For example, if you are assigned a role that cannot create a compute instance, the option to create a compute instance will not appear in the studio. For more information, see Manage users and roles.

Next steps

Visit the studio, or explore the different authoring options with these tutorials:

Start with Quickstart: Get started with Azure Machine Learning. Then use these resources to create your first experiment with your preferred method: