Azure Machine Learning

Enterprise-grade machine learning service to build and deploy models faster

Accelerate the end-to-end machine learning lifecycle

The Azure Machine Learning service empowers 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 machine learning.

Machine learning for all skills

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

End-to-end MLOps

Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete machine learning lifecycle.

State-of-the-art responsible machine learning

Responsible machine learning capabilities—understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the machine learning lifecycle with audit trials and datasheets.

Open and interoperable

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

Boost productivity with machine learning 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 with visual machine learning or built-in collaborative Jupyter Notebooks for a code-first experience. Accelerate model creation with automated machine learning, and access built-in feature engineering, algorithm selection, and hyperparameter sweeping to develop highly accurate models.

Operationalize at scale with MLOps

MLOps, or DevOps for machine learning, streamlines the machine learning lifecycle, from building models to deployment and management. Use machine learning pipelines to build repeatable workflows, and use a rich model registry to track your assets. Manage production workflows at scale 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 machine learning workflows at scale in an enterprise-ready fashion.

Build responsible machine learning solutions

Access state-of-the-art responsible machine learning capabilities to understand, protect, and control your data, models, and processes. Explain model behavior 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 machine learning assets. Automatically maintain audit trails, track lineage, and use model datasheets to enable accountability.

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, or 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 optimize and accelerate inferencing across cloud and edge devices.

Advanced security and governance

  • Get end-to-end security and build on the trusted cloud with Azure.
  • Protect your resources with granular role-based access, custom roles, and built-in mechanisms for identity authentication.
  • Build, train, and deploy models more 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.

Key service capabilities

Collaborative notebooks

Maximize productivity with IntelliSense, easy compute and kernel switching, and offline notebook editing.

Automated machine learning

Rapidly create accurate models for classification, regression, and time-series forecasting. Use model interpretability to understand how the model was built.

Drag-and-drop machine learning

Use machine learning tools like designer with modules for data transformation, model training, and evaluation, or to easily create and publish machine learning pipelines.

Data labeling

Prepare data quickly, manage and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.


Use the central registry to store and track data, models, and metadata. Automatically capture lineage and governance data. Use Git to track work and GitHub Actions to implement workflows. Manage and monitor runs, or compare multiple runs for training and experimentation.

Autoscaling compute

Use managed compute to distribute training and to rapidly test, validate, and deploy models. Share CPU and GPU clusters across a workspace and automatically scale to meet your machine learning needs.

RStudio support

Build and deploy models and monitor runs with built-in R support and RStudio Server (open source edition).

Deep integration with other Azure services

Accelerate productivity with built-in integration with Microsoft Power BI and Azure services such as Azure Synapse Analytics, Azure Cognitive Search, Azure Data Factory, Azure Data Lake, and Azure Databricks.

Reinforcement learning

Scale reinforcement learning to powerful compute clusters, support multi-agent scenarios, and access open-source reinforcement learning algorithms, frameworks, and environments.

Responsible machine learning

Get model transparency at training and inferencing with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. Protect data with differential privacy.

Enterprise-grade security

Build and deploy models more securely with network isolation and private link capabilities, role-based access control for resources and actions, custom roles, and managed identity for compute resources.

Cost management

Better manage resource allocations for Azure Machine Learning compute instances with workspace- and resource-level quota limits.

Pay only for what you need, with no upfront cost

See Azure Machine Learning pricing.

Mastering Azure Machine Learning

Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes.

Packt: Principles of Data Science

Many people working with data have developed skills in math, programming, or domain expertise, but proper data science calls for all three. This comprehensive e-book helps fill in the gaps.

Forrester Wave Leader 2020

Forrester names Microsoft and Azure Machine Learning a leader in The Forrester Wave™: Notebook-based Predictive Analytics and Machine Learning, Q3 2020.

How to use Azure Machine Learning

Go to your studio web experience

Build and train

Deploy and manage

Step 1 of 1

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. 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

"The model we deployed on Azure Machine Learning helped us choose the three new retail locations we opened in 2019. Those stores exceeded their revenue plans by over 200 percent in December, the height of our season, and within months of opening were among the best-performing stores in their districts."

Jolie Vitale, Director of BI and Analytics, Carhartt

By using Azure Machine Learning, Scandinavian Airlines (SAS) is accurately identifying fraud with proficiency that wasn’t possible through manual methods. In the case of retroactively registering a flight for EuroBonus miles—a common source of fraud—the new system predicts fraud with 99 percent accuracy.

Scandinavian Airlines

"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

"We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators."

Alex Mohelsky, Partner and Advisory Data, Analytic, and AI Leader, EY Canada

"The automated machine learning capabilities in Azure Machine Learning save our data scientists from doing a lot of time-consuming work, which reduces our time to build models from several weeks to a few hours."

Xiaodong Wang, CEO, TalentCloud

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 percent.
  • The Azure Machine Learning studio is the top-level resource for the machine learning service. It provides a centralized place for data scientists and developers to work with all the artifacts for building, training and deploying machine learning models.

Ready when you are—let’s set up your Azure free account