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 AI.
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
State-of-the-art fairness and model interpretability to build responsible AI solutions, with enhanced security and cost management for advanced governance and control
Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python and R
Boost productivity and access 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 or use built-in Jupyter notebooks for a code-first experience. Accelerate model creation with the automated machine learning UI and access built-in feature engineering, algorithm selection and hyperparameter sweeping to develop highly accurate models.
Operationalise at scale with robust 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 AI solutions
Access state-of-the-art technology for fairness and machine learning model transparency. Use model interpretability for explanations about predictions to better understand model behaviour. Reduce model bias by applying common fairness metrics, automatically making comparisons and using recommended mitigations.
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 optimise and accelerate inferencing across cloud and edge devices.
Advanced security, governance and control
- Build machine learning models using the enterprise-grade security, compliance and virtual network support of Azure.
- Protect your assets using built-in controls for identity, data and network access, including custom roles.
- Restrict access to only your corporate network or apply Azure security policies.
- Manage governance and controls with audit trail, quota and cost management and a comprehensive compliance portfolio.
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
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
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."
Matthieu Boujonnier, Analytics Application Architect and Data Scientist, Schneider Electric
"With Azure Machine Learning, we can focus our testing on the most accurate models and avoid testing a large range of less valuable models. That saves months of time."
Diana Kennedy, Vice President for IT Strategy, Architecture and Planning, BP
"A key part of our transformation has been to embrace the cloud and the digital solutions and services that come with it. This includes a deep dive into AI and machine learning."
Naeem Khedarun, Principal Software Engineer (AI), ASOS
"By unifying our tech stack and bringing our engineers in Big Data and online software together with data scientists, we got our development time down from months to just a few weeks."
Phil Harris, assistant professor of physics, MIT
"The [Large Hadron Collider in Europe] pushes technology on many fronts...and produces data rates that are the largest in the world. We are an example of how to do analysis of large datasets."
Borrowell helps consumers improve credit using AI
Borrowell’s innovative AI technology uses credit scores to deliver recommendations that improve the credit and financial well-being of its Canadian customers.
Azure Machine Learning updates, blogs and announcements
Azure Open Datasets, now in preview, offers access to curated datasets.
Azure Machine Learning available in US Gov
Integrate Azure Stream Analytics with Azure Machine Learning (in preview)
21 January, 2020
MLOps—the path to building a competitive edge
5 November, 2019
Azure Machine Learning—ML for all skill levels
Simplify ML workloads with Azure Machine Learning events now in Event Grid
28 October, 2019
Automated machine learning and MLOps with Azure Machine Learning
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 centralised place for data scientists and developers to work with all the artifacts for building, training and deploying machine learning models.