Azure Machine Learning

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

Accelerate the end-to-end machine learning life cycle

Empower data scientists and developers with a wide range of productive experiences to build, train, and deploy machine learning models and foster team collaboration. Accelerate time to market with industry-leading MLOps—machine learning operations, or DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning.

Machine Learning for all skill levels

Productivity for all skill levels, with Jupyter Notebooks, drag-and-drop designer, and automated machine learning

End-to-end MLOps

Robust MLOps capabilities that enable creation and deployments of models at scale using automated and reproducible machine learning workflows

Responsible machine learning innovation

Rich set of built-in responsible capabilities to understand, protect, and control data, models, and processes

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 skill levels

Rapidly build and deploy machine learning models using tools that meet your needs, regardless of skill level. Use built-in Jupyter Notebooks with Intellisense or the drag-and-drop designer. Accelerate model creation with automated machine learning, and access powerful feature engineering, algorithm selection and hyperparameter-sweeping capabilities. Increase team efficiency with shared datasets, notebooks, models and customisable dashboards that track all aspects of the machine learning process.

Operationalise at scale with MLOps

Take advantage of MLOps to streamline the machine learning lifecycle, from building models to deployment and management. Create reproducible workflows with machine learning pipelines, and train, validate, and deploy thousands of models at scale, from the cloud to the edge. Use managed online and batch endpoints to seamlessly deploy and score models without managing the underlying infrastructure. Use Azure DevOps or GitHub Actions to schedule, manage, and automate the machine learning pipelines, and use advanced data-drift analysis to improve model performance over time.

Build responsible machine learning solutions

Access state-of-the-art responsible machine learning capabilities to understand, control, and help protect 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, Visual Studio Code, Jupyter Notebooks, and CLIs, or languages such as Python and R. Use ONNX Runtime to optimize and accelerate inferencing across cloud and edge devices. Track all aspects of your training experiments using MLflow.

Build your machine learning skills with Azure

Learn more about machine learning on Azure and participate in hands-on tutorials with this 30-day learning journey. At the end of this learning journey, you’ll be prepared to take the Azure Data Scientist Associate certification.

Advanced security, governance and hybrid infrastructure

  • Train models on your hybrid infrastructure using Kubernetes clusters on-premises, across multicloud environments and at the edge with Azure Arc interoperability.
  • Access security capabilities such as role-based access, custom machine learning roles, 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. Launch your notebook in Visual Studio Code for a rich development experience, including secure debugging and support for Git source control.

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

Data labelling

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

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. Use managed endpoints to operationalize model deployment and scoring, log metrics, and perform safe model rollouts.

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.

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, Azure Arc and Azure Databricks.

Hybrid and multicloud support

Run machine learning on existing Kubernetes clusters on-premises, in multicloud environments, and at the edge with Azure Arc. Use the one-click machine learning agent to start training models more securely, wherever your data lives.

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. Help protect data with differential privacy and confidential machine learning pipelines.

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.

Only pay 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.

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.

A Forrester Wave Leader 2020

Forrester names Microsoft 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 three new retail locations. Those stores exceeded their revenue plans by over 200 percent [that] 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

"MLOps is at the core of our product. Because of its reproducible ML pipelines, ... registered models, and automatic model scoring, we're definitely detecting things that we missed before. Which, in terms of risk management, is really, really important."

Ignasi Paredes-Oliva, Lead Data Scientist, Nestlé Global Security Operations Center
Nestle Italia

"Azure Machine Learning allows us to manage the entire lifecycle, from experimentation and development to production and enhancements."

Joey Chua, Senior Manager Machine Learning Engineering, AGL
AGL

"With model interpretability in Azure Machine Learning, we have a high degree of confidence that our machine learning model is generating meaningful and fair results."

Daniel Engberg, Head of Data Analytics and Artificial Intelligence, Scandinavian Airlines
Scandinavian Airlines

"We've used the MLOps capabilities in Azure Machine Learning to simplify the whole machine learning process. That allows us to focus more on data science and let Azure Machine Learning take care of end-to-end operationalization."

Michael Cleavinger, Senior Director of Shopper Insights Data Science and Advanced Analytics, PepsiCo
PepsiCo

"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

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 uptime.
  • Azure Machine Learning studio is the top-level resource for Machine Learning. This capability provides a centralised place for data scientists and developers to work with all the artifacts for building, training and deploying machine learning models.

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