Azure Machine Learning service

Build models rapidly and operationalize at scale from cloud to edge

Accelerate the end-to-end machine learning lifecycle

Streamline the building, training, and deployment of machine learning models. Bring machine learning models to market faster using the tools and frameworks of your choice, increase productivity using automated machine learning, and innovate on a secure, enterprise-ready platform.

Simplified machine learning with powerful, no-code, automated machine learning capabilities and open-source support

Robust DevOps for machine learning that integrates with your existing DevOps processes and helps manage the complete machine learning lifecycle

Scale on demand from your desktop, and build and deploy machine learning models anywhere, from cloud to edge

Enterprise-ready Azure security, control, and governance to help protect your infrastructure and capabilities

Access simplified machine learning

Rapidly build and deploy machine learning models using tools that meet your needs across skill levels, from no-code to code-first experiences. Use a visual drag-and-drop interface, a hosted notebook environment, or automated machine learning. Accelerate model development with automated feature engineering, algorithm selection, and hyperparameter sweeping. Get built-in support for familiar open-source tools and frameworks, including ONNX, Python, PyTorch, scikit-learn, and TensorFlow.

Innovate faster with robust MLOps

MLOps—DevOps for machine learning—streamlines the end-to-end lifecycle, from data preparation to deployment and monitoring. Simplify your workflows and increase efficiency using machine learning pipelines. Take advantage of continuous integration and continuous delivery (CI/CD) for ease of support and maintenance while improving model quality over time. Manage your model artifacts from a central portal, and monitor the performance of deployed models.

Tap into the cloud on demand from your desktop

Use any data and deploy machine learning models anywhere, from the cloud to the edge, to maximize flexibility. Train models quickly and cost-effectively by autoscaling using powerful CPU and GPU compute resources. Inference in real time in the cloud or at the edge using FPGAs.

Protect your infrastructure and solutions

Build your machine learning models using the enterprise-ready security, compliance, and virtual network support of Azure for all your data science needs. Protect your workloads using built-in controls for identity, data, and networking across Azure, which offers the most comprehensive compliance portfolio of any cloud provider.

Pay only for what you need, with no upfront cost

Pay only for the Azure resources you need for a limited time. For more details, including the cost of deploying models, see the Azure Machine Learning service pricing page.

How to use Azure Machine Learning service

Create a workspace

Build and train

Deploy and manage

Step 1 of 1

Store your compute targets, models, deployments, metrics, and run histories in the cloud.

Step 1 of 1

Use automated machine learning to identify algorithms and hyperparameters and track experiments in the cloud.

Step 1 of 1

Deploy your machine learning model to the cloud or the edge, monitor performance, and retrain it as needed.

Getting Started Resources

First experiment

After you create a workspace for Azure Machine Learning service, learn how to run an experiment with the Python SDK or by using the visual interface.

Tutorials and samples

Train and deploy machine learning models on remote compute resources. Use the SDK for image classification (MNIST data) or regression (NYC taxi data), or use visual interface to predict prices (automotive data).

Start using Azure Machine Learning service today

Get instant access and a $200 credit by signing up for an Azure free account.

Sign in to the Azure portal.

Customers using Azure Machine Learning service

Walgreens Boots Alliance
Schneider Electric

Azure updates, blogs, and announcements

Frequently asked questions about Azure Machine Learning service

  • The service is generally available in several countries/regions, with more on the way.
  • The service-level agreement (SLA) for Azure Machine Learning service is 99.9 percent.
  • The Azure Machine Learning service workspace is the top-level resource for the service. It provides a centralized place to work with all the artifacts you create.

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