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:
- How to author machine learning projects in the studio.
- How to manage assets and resources in the studio.
- The differences between Azure Machine Learning studio and ML Studio (classic).
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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.
Write and run your own code in managed Jupyter Notebook servers that are directly integrated in the studio.
Azure Machine Learning designer
Use the designer to train and deploy machine learning models without writing any code. Drag and drop datasets and modules to create ML pipelines. Try out the designer tutorial.
Automated machine learning UI
Learn how to create automated ML experiments with an easy-to-use interface.
Use Azure Machine Learning data labeling to efficiently coordinate data 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:
- Compute resources
- Run logs
- 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. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.
- See information on moving machine learning projects from ML Studio (classic) to Azure Machine Learning.
- Learn more about Azure Machine Learning
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.
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.
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: