Azure Machine Learning

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

Accelerate the end-to-end machine learning lifecycle

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.

ML for all skills

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

State-of-the-art Responsible ML

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

State-of-the-art Responsible ML

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.

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 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 with visual machine learning or built-in collaborative Jupyter notebooks for a code-first experience. Accelerate model creation with the 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 ML 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 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 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 ML 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, 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 optimize 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.

Key service capabilities

Collaborative Notebooks

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

Automated ML

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

Drag and Drop ML

Use designer with modules for data transformation, model training and evaluation, or to create and publish ML pipelines with a few clicks.

Data Labeling

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

MLOps

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 rapidly test, validate and deploy models. CPU and GPU clusters can be shared across a workspace and automatically scale to meet your ML needs.

RStudio integration

Built in R support and RStudio Server (Open Source edition) integration to build and deploy models and monitor runs.

Deep integration with other Azure services

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

Reinforcement learning

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

Responsible ML

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 securely with capabilities like network isolation and Private Link, 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 with workspace and resource level quota limits.

Pay only 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

"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
Carhartt

Scandinavian Airlines

By using Azure Machine Learning, 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
EY

"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: Chief Executive Officer, TalentCloud
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