What is Azure Machine Learning?
Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps.
You can create a model in Azure Machine Learning or use a model built from an open-source platform, such as Pytorch, TensorFlow, or scikit-learn. MLOps tools help you monitor, retrain, and redeploy models.
Azure Machine Learning is for individuals and teams implementing MLOps within their organization to bring machine learning models into production in a secure and auditable production environment.
Data scientists and ML engineers will find tools to accelerate and automate their day-to-day workflows. Application developers will find tools for integrating models into applications or services. Platform developers will find a robust set of tools, backed by durable Azure Resource Manager APIs, for building advanced ML tooling.
Enterprises working in the Microsoft Azure cloud will find familiar security and role-based access control (RBAC) for infrastructure. You can set up a project to deny access to protected data and select operations.
Machine learning projects often require a team with varied skillsets to build and maintain. Azure Machine Learning has tools that help enable collaboration, such as:
Developers find familiar interfaces in Azure Machine Learning, such as:
The Azure Machine Learning studio is a graphical user interface for a project workspace. In the studio, you can:
Plus, the designer has a drag-and-drop interface where you can train and deploy models.
If you’re a ML Studio (classic) user, learn about Studio (classic) deprecation and the difference between it and Azure Machine Learning studio.
Azure Machine Learning integrates with the Azure cloud platform to add security to ML projects.
Other integrations with Azure services support a machine learning project from end-to-end. They include:
Typically models are developed as part of a project with an objective and goals. Projects often involve more than one person. When experimenting with data, algorithms, and models, development is iterative.
While the project lifecycle can vary by project, it will often look like this:
A workspace organizes a project and allows for collaboration for many users all working toward a common objective. Users in a workspace can easily share the results of their runs from experimentation in the studio user interface or use versioned assets for jobs like environments and storage references.
For more information, see Manage Azure Machine Learning workspaces.
When a project is ready for operationalization, users’ work can be automated in a machine learning pipeline and triggered on a schedule or HTTPS request.
