Azure Machine Learning
Enterprise-grade machine learning service for building and deploying models faster
Accelerate the end-to-end machine learning life cycle
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.
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.
Operationalise at scale with MLOps
MLOps, or DevOps for machine learning, streamlines the machine learning life cycle, 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 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 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 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 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 such as 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 optimise 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
Maximise 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 such as designer with modules for data transformation, model training and evaluation, or to easily create and publish machine learning pipelines.
Prepare data quickly, manage and monitor labelling projects, and automate iterative tasks with machine learning-assisted labelling.
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 to rapidly test, validate and deploy models. Share CPU and GPU clusters across a workspace and automatically scale to meet your machine learning needs.
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.
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.
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.
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
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
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, CEO, 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.
The Azure Quota REST API to manage service limits (quotas) is now generally available
Azure Machine Learning updates December 2020 in public preview
Azure Machine Learning updates--November 2020
Azure Machine Learning offers added capabilities at lower cost
Azure Machine Learning updates Ignite 2020
Azure Machine Learning announces output dataset (Preview)
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.