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.
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 lifecycle
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 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
Maximize productivity with intellisense, easy compute and kernel switching and offline notebook editing.
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.
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.
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.
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.
Scale reinforcement learning to powerful compute clusters, support multi-agent scenarios, access open source RL algorithms, frameworks and environments.
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.
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.
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
How to use Azure Machine Learning
Go to your studio web experience
Build and train
Deploy and manage
You can author new models and store your compute targets, models, deployments, metrics, and run histories in the cloud.
Start using Azure Machine Learning today
Customers using Azure Machine Learning
Jolie Vitale: Director of BI and Analytics, Carhartt
"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."
Dean Riddlesden, Senior Data Scientist, Global Analytics, Walgreens Boots Alliance
"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."
Alex Mohelsky: Partner and Advisory Data, Analytic, and AI Leader, EY Canada
"We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators."
Xiaodong Wang: Chief Executive Officer, TalentCloud
"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."
Azure Machine Learning updates, blogs, and announcements
Azure Open Datasets, now in preview, offers access to curated datasets.
Azure Machine Learning offers added capabilities at lower cost
Azure Machine Learning updates Ignite 2020
Azure Machine Learning announces output dataset (Preview)
Azure Machine Learning studio web experience is generally available
May 21, 2020
Meeting the challenges of today and tomorrow with Azure AI
New Responsible ML innovation in Azure Machine Learning
Azure Machine Learning – what’s new from Build 2020
May 19, 2020
Build AI you can trust with responsible ML
Azure Machine Learning—New log streaming feature is now available
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.