Azure Machine Learning
Use an enterprise-grade service for the end-to-end machine learning lifecycle
Azure Machine Learning
Use an enterprise-grade service for the end-to-end machine learning lifecycle
Build business-critical machine learning models at scale
Empower data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. Accelerate time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools. Innovate on a secure, trusted platform designed for responsible AI applications in machine learning.
Rapidly build and train models
Use the studio development experience to access built-in tools and best-in-class support for open-source frameworks and libraries.
Deliver responsible solutions
Develop models for fairness and explainability, use them responsibly when deployed, and govern to fulfill lineage and audit compliance requirements.
Operationalize at scale
Deploy ML models quickly and easily, and manage and govern them efficiently, with MLOps.
Innovate on a more secure hybrid platform
Run machine learning workloads anywhere with built-in governance, security, and compliance.
Up to 3 times the ROI on machine learning projects
70 percent fewer steps for training models
90 percent fewer lines of code for pipelines
60 compliance certifications
The only platform with PyTorch Enterprise
Support for the end-to-end machine learning lifecycle
Label training data and manage labeling projects.
Use with analytics engines for data exploration and preparation.
Access data and create and share datasets.
Use collaborative Jupyter notebooks with attached compute.
Automated machine learning
Automatically train and tune accurate models.
Design with a drag-and-drop development interface.
Run experiments and create and share custom dashboards.
Command line interface
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.
Develop in a managed and secure environment with cloud CPUs, GPUs, and supercomputing clusters.
Open-source libraries and frameworks
Get built-in support for Scikit-learn, PyTorch, TensorFlow, Keras, Ray RLLib, and more.
Deploy models for batch and real-time inference quickly and easily.
Pipelines and CI/CD
Automate machine learning workflows.
Access container images with frameworks and libraries for inference.
Share and track models and data.
Hybrid and multicloud
Train and deploy models on premises and across multicloud environments.
Accelerate training and inference and lower costs with ONNX Runtime.
Monitoring and analysis
Track, log, and analyze data, models, and resources.
Detect drift and maintain model accuracy.
Debug models and optimize model accuracy.
Trace machine learning artifacts for compliance.
Use built-in and custom policies for compliance management.
Enjoy continuous monitoring with Azure Security Center.
Apply quota management and automatic shutdown.
Accelerate time to value with rapid and accurate model development
Improve productivity with the studio capability, a development experience that supports all machine learning tasks, to build, train, and deploy models. Collaborate with Jupyter Notebooks using built-in support for popular open-source frameworks and libraries. Create accurate models quickly with automated machine learning using feature engineering and hyperparameter-sweeping capabilities. Access the debugger, profiler, and explanations to improve model performance as you train. Use deep Visual Studio Code to go from local to cloud training seamlessly, and autoscale with powerful cloud-based CPU and GPU clusters.
Operationalize at scale with machine learning operations (MLOps)
Streamline the deployment and management of thousands of models on premises, at the edge, and in multicloud environments using MLOps. Deploy and score ML models faster with fully managed endpoints for batch and real-time predictions. Use repeatable pipelines to automate workflows for continuous integration and continuous delivery (CI/CD). Continuously monitor model performance metrics, detect data drift, and trigger retraining to improve model performance. Throughout the lifecycle, enable auditability and governance with built-in tracking and lineage for all machine learning artifacts.
Deliver responsible machine learning solutions
Evaluate machine learning models with reproducible and automated workflows to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis. Make real-life interventions and policies with causal analysis in the responsible AI dashboard and generate a scorecard at deployment time. Export the scorecard to a PDF to contextualize responsible AI metrics, and share it with both technical and non-technical audiences to involve stakeholders and streamline compliance review.
Innovate on a hybrid platform that's more secure and compliant
Increase security across the machine learning lifecycle with comprehensive capabilities spanning identity, authentication, data, networking, monitoring, governance, and compliance. Build more secure machine learning solutions using custom role-based access control, virtual networks, data encryption, private endpoints, and end-to-end private IP addresses. Train and deploy models on premises to meet data sovereignty requirements. Manage governance with built-in policies and streamline compliance with a comprehensive portfolio spanning 60 certifications, including FedRAMP High and HIPAA.
Build your machine learning skills with Azure
Learn more about machine learning on Azure and participate in hands-on tutorials with a 30-day learning journey. By the end, you'll be prepared to take the Azure Data Scientist Associate Certification.
Key service capabilities for the full machine learning lifecycle
Create, manage, and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.
Perform interactive data preparation with PySpark using Azure Synapse Analytics.
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.
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.
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.
Scale reinforcement learning to powerful compute clusters, support multiple-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. Improve model reliability and identify and diagnose model errors with the error analysis toolkit. Help protect data with differential privacy.
Manage and monitor runs or compare multiple runs for training and experimentation. Create custom dashboards and share them with your team.
Model registry and audit trail
Use the central registry to store and track data, models, and metadata. Automatically capture lineage and governance data with audit trail.
Git and GitHub
Use Git integration to track work and GitHub Actions support to implement machine learning workflows.
Use managed endpoints to operationalize model deployment and scoring, log metrics, and perform safe model rollouts.
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.
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.
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.
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.
Reduce IT costs and better manage resource allocations for compute instances, with workspace and resource-level quota limits and automatic shutdown.
Learn expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes.
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.
See why Forrester named Azure Machine Learning a Leader in The Forrester WaveTM: Notebook-Based Predictive Analytics And Machine Learning, Q3 2020.
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.
Learn how to build secure, scalable, and equitable solutions.
Read about tools and methods to understand, protect, and control your models.
Accelerate the process of building, training, and deploying models at scale.
Learn how to build, train, and deploy models in any infrastructure.
Comprehensive security and compliance, built in
Microsoft invests more than $1 billion annually on cybersecurity research and development.
We employ more than 3,500 security experts dedicated to data security and privacy.
Azure has more certifications than any other cloud provider. View the comprehensive list.
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.
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.
Pay only for what you need, with no upfront cost
Get started with an Azure free account
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 40+ other services that are always free.
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.
After 12 months, you'll keep getting 40+ always-free services—and still pay only for what you use beyond your free monthly amounts.
Customers using Azure Machine Learning
Nic Bourven, Chief Information Officer, AXA UK
"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."
Bikram Virk, Product Manager, AI and Machine Learning, FedEx
"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."
Dr. Deepa Kasinathan, Product Manager and Group Leader, Robotron Datenbank-Software GmbH
"The end-to-end pipeline (built with Azure Machine Learning) has all the features needed to develop and maintain machine learning models throughout their lifecycles."
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."
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."
Making life easier for rail passengers
DB Systel, a partner of German rail company Deutsche Bahn, developed the Digital Guide Dog solution to assist passengers. Using Azure Machine Learning, it takes just a few hours to train a new model using neural networks.
Azure Machine Learning resources
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 percent uptime.
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.