Azure Machine Learning

Enterprise-grade machine learning service for building and deploying models faster

Accelerate the end-to-end machine learning life cycle

Empower developers and data scientists with a wide range of productive experiences for building, training and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps – DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML.

Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning

Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML life cycle

Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets

Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python and R

Boost productivity and access ML for all skills

Rapidly build and deploy machine learning models using tools that meet your needs, regardless of skill level. Use the no-code designer to get started, or use built-in Jupyter notebooks for a code-first experience. Accelerate model creation with the automated machine learning UI, and access built-in feature engineering, algorithm selection and hyperparameter sweeping to develop highly accurate models.

Operationalise at scale with robust MLOps

MLOps, or DevOps for machine learning, streamlines the machine learning life cycle, from building models to deployment and management. Use ML pipelines to build repeatable workflows, and use a rich model registry to track your assets. Manage production workflows at scale by using advanced alerts and machine learning automation capabilities. Profile, validate and deploy machine learning models anywhere, from the cloud to the edge, to manage production ML workflows at scale in an enterprise-ready fashion.

Build responsible ML solutions

Access state-of-the-art responsible ML capabilities to understand protect and control your data, models and processes. Explain model behaviour during training and inferencing, and build for fairness by detecting and mitigating model bias. Preserve data privacy throughout the machine learning lifecycle with differential privacy techniques and use confidential computing to secure ML assets. Apply policies, use lineage and manage and control resources to meet regulatory standards.

Innovate on an open and flexible platform

Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow and scikit-learn, or the open and interoperable ONNX format. Choose the development tools that best meet your needs, including popular IDEs, Jupyter notebooks and CLIs – or languages such as Python and R. Use ONNX Runtime to optimise and accelerate inferencing across cloud and edge devices.

Advanced security and governance

  • Get the security from the ground up and build on the trusted cloud with Azure.
  • Protect access to your resources with granular role-based access, custom roles and built-in mechanisms for identity authentication.
  • Build train and deploy models securely by isolating your network with virtual networks and private links.
  • Manage governance with policies, audit trails, quota and cost management.
  • Streamline compliance with a comprehensive portfolio spanning 60 certifications including FedRAMP High and DISA IL5.

Only pay for what you need, with no upfront cost

For details, go to the Azure Machine Learning pricing page.

How to use Azure Machine Learning

Go to your studio web experience

Build and train

Deploy and manage

Step 1 of 1

You can author new models and 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. You can also author models using notebooks or the drag-and-drop designer.

Step 1 of 1

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

Start using Azure Machine Learning 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

"If I have 200 models to train—I can just do this all at once. It can be farmed out to a huge compute cluster, and it can be done in minutes. So I'm not waiting for days."

Dean Riddlesden, Senior Data Scientist, Global Analytics, Walgreens Boots Alliance
Walgreens Boots Alliance

"With Azure Machine Learning, we can focus our testing on the most accurate models and avoid testing a large range of less valuable models. That saves months of time."

Matthieu Boujonnier, Analytics Application Architect and Data Scientist, Schneider Electric
Schneider Electric

"A key part of our transformation has been to embrace the cloud and the digital solutions and services that come with it. This includes a deep dive into AI and machine learning."

Diana Kennedy, Vice President for IT Strategy, Architecture and Planning, BP

"By unifying our tech stack and bringing our engineers in Big Data and online software together with data scientists, we got our development time down from months to just a few weeks."

Naeem Khedarun, Principal Software Engineer (AI), ASOS

"The [Large Hadron Collider in Europe] pushes technology on many fronts...and produces data rates that are the largest in the world. We are an example of how to do analysis of large datasets."

Phil Harris, Assistant Professor of Physics, MIT

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Azure Machine Learning updates, blogs and announcements

Frequently asked questions about Azure Machine Learning

  • The service is generally available in several countries/regions, with more on the way.
  • The service-level agreement (SLA) for Azure Machine Learning is 99.9 per cent.
  • The Azure Machine Learning studio is the top-level resource for the machine learning service. It provides a centralised place for data scientists and developers to work with all the artefacts for building, training and deploying machine learning models.

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