Azure Machine Learning
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 MLOps (machine learning operations), open-source interoperability, and integrated tools. Innovate on a secure, trusted platform designed for responsible machine learning (ML).
Rapidly build and train models
Use the studio development experience to access integrated tools and best-in-class support for open-source frameworks and libraries.
Operationalize at scale
Deploy models with a single click and manage and govern them efficiently with MLOps.
Deliver responsible solutions
Understand and protect data and models, build for fairness, and improve model quality.
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 ML 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 (ML) lifecycle
Label training data and manage labeling projects.
Integrate with analytics engines for data exploration and preparation.
Access data and create and share datasets.
Use collaborative Jupyter notebooks with attached compute.
Automatically train and tune accurate models.
Design with drag-and-drop development interface.
Run experiments and create and share custom dashboards.
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.
Utilize one-click deployment for batch and real-time inference.
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.
Accelerate training and inference and lower costs with ONNX Runtime.
Monitor and analyze
Track, log, and analyze data, models, and resources.
Detect drift and maintain model accuracy.
Debug models and optimize model accuracy.
Trace ML artifacts for compliance.
Leverage 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 studio, the development experience that supports all ML 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 ML, 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 integration 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 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. And throughout the lifecycle, enable auditability and governance with out-of-the-box tracking and lineage for all ML artifacts.
Deliver responsible machine learning solutions
Access industry-leading responsible AI capabilities to increase model transparency and improve reliability. Understand models using out-of-the-box visualizations and use what-if-analysis to determine feature impact on predictions. Share model explanation graphs with your team to ensure compliance. Use state-of-the-art algorithms to test models for fairness issues, compare different models, and take steps to mitigate the issues. Identify and debug model errors with the error analysis toolkit to improve model accuracy.
Innovate on a hybrid platform that is more secure and compliant
Increase security across the ML lifecycle with comprehensive capabilities spanning identity, authentication, data, networking, monitoring, governance, and compliance. Build more secure ML 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 this 30-day learning journey. By the end, you'll be prepared for the Azure Data Scientist Associate Certification.
Key service capabilities for the full ML lifecycle
Create, manage, and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.
Perform interactive data preparation with PySpark, using built-in integration with 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, and time-series forecasting. 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 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 ML 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.
Deep integration with other Azure services
Accelerate productivity with built-in integration with 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 one-click 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.
Enable IT to reduce cost and better manage resource allocations for compute instances with workspace and resource-level quota limits and automatic shutdown.
Pay only 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.
Discover a systematic approach to building, deploying, and monitoring machine learning solutions with MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale.
Forrester names Microsoft Azure Machine Learning a leader in The Forrester Wave™: Notebook-Based Predictive Analytics And Machine Learning, Q3 2020.
Forrester Total Economic Impact™ (TEI), a commissioned study conducted by Forrester Consulting, provides a framework to evaluate the potential financial impact of Azure Machine Learning on their organizations.
Empowering organizations to build secure, scalable, and equitable ML solutions with Azure Machine Learning.
Tools and methods to understand, protect, and control your models.
Accelerating the process of building, training, and deploying models at scale.
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, and 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.
Start using Azure Machine Learning today
Customers using Azure Machine Learning
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—the digital partner of the German rail company Deutsche Bahn—developed a solution called Digital Guide Dog to assist passengers. Using Microsoft Azure Machine Learning, it takes just a few hours to train a new model using neural networks.
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.