Azure Machine Learning
Use an enterprise-grade service for the end-to-end machine learning lifecycle.
Business-critical machine learning models at scale
Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools. This trusted platform is designed for responsible AI applications in machine learning.
Rapid model development and training, with integrated tools and support scalable purpose-built AI infrastructure.
Responsible AI model development with built-in fairness and explainability, and responsible usage for compliance
Quick ML model deployment, management, and sharing for cross-workspace collaboration and MLOps
Built-in governance, security, and compliance for running machine learning workloads anywhere
Support for the end-to-end machine learning lifecycle
Data labeling
Label training data and manage labeling projects.
Data preparation
Use with analytics engines for data exploration and preparation.
Datasets
Access data and create and share datasets.
Notebooks
Use collaborative Jupyter notebooks with attached compute.
Automated machine learning
Automatically train and tune accurate models.
Drag-and-drop designer
Design with a drag-and-drop development interface.
Experiments
Run experiments and create and share custom dashboards.
CLI and Python SDK
Accelerate the model training process while scaling up and out on Azure compute.
Visual Studio Code and GitHub
Use familiar tools and switch easily from local to cloud training.
Compute instance
Develop in a managed and secure environment with dynamically scalable CPUs, GPUs, and supercomputing clusters.
Open-source libraries and frameworks
Get built-in support for Scikit-learn, PyTorch, TensorFlow, Keras, Ray RLLib, and more.
Managed endpoints
Deploy models for batch and real-time inference quickly and easily.
Pipelines and CI/CD
Automate machine learning workflows.
Prebuilt images
Access container images with frameworks and libraries for inference.
Model repository
Share and track models and data.
Hybrid and multicloud
Train and deploy models on premises and across multicloud environments.
Optimize models
Accelerate training and inference and lower costs with ONNX Runtime.
Monitoring and analysis
Track, log, and analyze data, models, and resources.
Data drift
Detect drift and maintain model accuracy.
Error analysis
Debug models and optimize model accuracy.
Auditing
Trace machine learning artifacts for compliance.
Policies
Use built-in and custom policies for compliance management.
Security
Enjoy continuous monitoring with Azure Security Center.
Cost control
Apply quota management and automatic shutdown.
Azure Machine Learning for Deep Learning
Managed end-to-end platform
Streamline the entire deep learning lifecycle and management of models with native MLOps capabilities. Run machine learning anywhere securely with enterprise-grade security. Mitigate model biases and evaluate models with the Responsible AI dashboard.
Any development tools and frameworks
Build deep learning models with your favorite IDEs from Visual Studio Code to Jupyter Notebooks and in the framework of your choice with PyTorch and TensorFlow. Azure Machine Learning integrates with ONNX Runtime and DeepSpeed to optimize your training and inference.
World Class Performance
Leverage purpose-built AI infrastructure uniquely designed to combine the latest NVIDIA GPUs and Mellanox Networking up to 200GB/s InfiniBand interconnects. Scale up to thousands of GPUs within a single cluster with unprecedented scale.
Accelerate time to value with rapid model development
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Operationalize at scale with MLOps
Deliver responsible machine learning solutions
Innovate on a hybrid platform that's more secure and compliant
Build your machine learning skills with Azure
Key service capabilities for the full machine learning lifecycle
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Data labeling
Create, manage, and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.
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Data preparation
Quickly iterate on data preparation at scale on Apache Spark clusters within Azure Machine Learning, interoperable with Azure Synapse Analytics.
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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.
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Automated machine learning
Rapidly create accurate models for classification, regression, time-series forecasting, natural language processing tasks, and computer vision tasks. Use model interpretability to understand how the model was built.
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Drag-and-drop machine learning
Use machine learning tools like designer for data transformation, model training, and evaluation, or to easily create and publish machine learning pipelines.
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Reinforcement learning
Scale reinforcement learning to powerful compute clusters, support multiple-agent scenarios, and access open-source reinforcement-learning algorithms, frameworks, and environments.
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Responsible building
Get model transparency at training and inferencing with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. Improve model reliability and identify and diagnose model errors with the error analysis toolkit. Help protect data with differential privacy.
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Experimentation
Manage and monitor runs or compare multiple runs for training and experimentation. Create custom dashboards and share them with your team.
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Registries
Use organization-wide repositories to store and share models, pipelines, components, and datasets across multiple workspaces. Automatically capture lineage and governance data using the audit trail feature.
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Git and GitHub
Use Git integration to track work and GitHub Actions support to implement machine learning workflows.
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Managed endpoints
Use managed endpoints to operationalize model deployment and scoring, log metrics, and perform safe model rollouts.
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Autoscaling compute
Use purpose-built AI supercomputers to distribute deep learning 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.
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Interoperability with other Azure services
Accelerate productivity with Microsoft Power BI and services such as Azure Synapse Analytics, Azure Cognitive Search, Azure Data Factory, Azure Data Lake, Azure Arc, Azure Security Center, and Azure Databricks.
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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 simple machine learning agent to start training models more securely, wherever your data lives.
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Enterprise-grade security
Build and deploy models more securely with network isolation and end-to-end private IP capabilities, role-based access control for resources and actions, custom roles, and managed identity for compute resources.
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Cost management
Reduce IT costs and better manage resource allocations for compute instances, with workspace and resource-level quota limits and automatic shutdown.
Comprehensive security and compliance, built in
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Microsoft invests more than $1 billion annually on cybersecurity research and development.
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We employ more than 3,500 security experts who are dedicated to data security and privacy.
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Azure has more certifications than any other cloud provider. View the comprehensive list.
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Pay only for what you need, with no upfront cost
Get started with an Azure free account
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Start free. Get $200 credit to use within 30 days. While you have your credit, get free amounts of many of our most popular services, plus free amounts of 55+ other services that are always free.
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After your credit, move to pay as you go to keep building with the same free services. Pay only if you use more than your free monthly amounts.
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After 12 months, you'll keep getting 55+ always-free services—and still pay only for what you use beyond your free monthly amounts.
Author new models and store your compute targets, models, deployments, metrics, and run histories in the cloud.
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.
Deploy your machine learning model to the cloud or the edge, monitor performance, and retrain it as needed.
Customers using Azure Machine Learning
"We make it our mission to try new ideas and go beyond to differentiate AXA UK from other insurers. We see managed endpoints in Azure Machine Learning as a key enabler for our digital ambition."
Nic Bourven, Chief Information Officer, AXA UK
"Customers expect timely and accurate information on their packages and a data-based delivery experience. We're helping FedEx stay on the leading edge with Azure Machine Learning, and we're building expertise for future projects."
Bikram Virk, Product Manager, AI and Machine Learning, FedEx
"As more of our groups rely on the Azure Machine Learning solution, our finance experts can focus more on higher-level tasks and spend less time on manual data collection and input."
Jeff Neilson, Data Science Manager, 3M
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"With Azure Machine Learning, we can show the patient a risk score that is highly tailored to their individual circumstances. …Ultimately, we aim to reduce risk, reduce uncertainty, and improve surgical outcomes."
Professor Mike Reed, Clinical Director, Trauma & Orthopedics, Northumbria Healthcare NHS Foundation Trust
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"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
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"Using automated machine learning features of Azure Machine Learning for machine learning model creation enabled us to realize an environment in which we can create and experiment with various models from multiple perspectives."
Keiichi Sawada, Corporate Transformation Division, Seven Bank
Azure Machine Learning resources
Advanced tutorials
Train and deploy automated machine learning models
Explore MLOps examples in GitHub
Use the designer tool for prediction
Interpret and explain machine learning models
Interpret and explain automated machine learning models
Use the Python SDK for automated machine learning
Use the automated machine learning UI
Auto-train a time-series model
Mastering Azure Machine Learning guide
Learn expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes.
Engineering MLOps white paper
Discover a systematic approach to building, deploying, and monitoring machine learning solutions with MLOps. Rapidly build, test, and manage production-ready machine learning lifecycles at scale.
Forrester Total Economic ImpactTM (TEI) study
The Forrester Consulting Total Economic ImpactTM (TEI) study, commissioned by Microsoft, examines the potential return on investment (ROI) enterprises may realize with Azure Machine Learning.
Machine Learning solutions white paper
Learn how to build secure, scalable, and equitable solutions.
Responsible AI white paper
Read about tools and methods to understand, protect, and control your models.
Machine learning operations (MLOps) white paper
Accelerate the process of building, training, and deploying models at scale.
Azure Arc–enabled Machine Learning white paper
Learn how to build, train, and deploy models in any infrastructure.
Frequently asked questions about Azure Machine Learning
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The service is generally available in several countries/regions, with more on the way.
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The service-level agreement (SLA) for Azure Machine Learning is 99.9 percent uptime.
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Azure Machine Learning studio is the top-level resource for Machine Learning. This capability provides a centralized place for data scientists and developers to work with all the artifacts for building, training, and deploying machine learning models.