Azure Machine Learning
Enterprise-grade machine learning service to build and deploy 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.
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 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, Visual Studio Code, Jupyter Notebooks and CLIs, or languages such as Python and R. Use ONNX Runtime to optimise 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
Maximise 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.
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 endpoints to operationalise model deployment and scoring, log metrics and perform safe model roll-outs.
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 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.
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.
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
Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark and Kubernetes.
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 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
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 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."
Ignasi Paredes-Oliva, Lead Data Scientist, Nestlé Global Security Operations Center
"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."
Joey Chua, Senior Manager Machine Learning Engineering, AGL
"Azure Machine Learning allows us to manage the entire lifecycle, from experimentation and development to production and enhancements."
Michael Cleavinger, Senior Director of Shopper Insights Data Science and Advanced Analytics, PepsiCo
"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."
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."
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.