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Azure Machine Learning

Use an enterprise-grade service for the end-to-end machine learning lifecycle

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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.

Check out what's new with Azure AI at Build

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

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.

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.

Compute instance

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.

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.

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.

Explore machine learning tools

Key service capabilities for the full machine learning lifecycle

Data labeling

Create, manage, and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.

Data preparation

Perform interactive data preparation with PySpark using Azure Synapse Analytics.

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.

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.

Reinforcement learning

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.

Experimentation

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.

Managed endpoints

Use managed endpoints to operationalize model deployment and scoring, log metrics, and perform safe model rollouts.

Autoscaling compute

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.

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.

Cost management

Reduce IT costs and better manage resource allocations for compute instances, with workspace and resource-level quota limits and automatic shutdown.

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.

The Forrester Wave^TM 2020 report

See why Forrester named Azure Machine Learning a Leader in The Forrester Wave^TM: Notebook-Based Predictive Analytics And Machine Learning, Q3 2020.

Forrester Total Economic Impact^TM (TEI) study

The Forrester Consulting Total Economic Impact^TM (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.

Comprehensive security and compliance, built in

Microsoft invests more than USD 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.

ISO/IEC

CSA/CCM

ITAR

CJIS

HIPAA

IRS 1075

Learn more about security on Azure

How to use Azure Machine Learning

Go to your studio web experience

Build and train

Deploy and manage

Step 1 of 1

Author new models and store your compute targets, models, deployments, metrics, and run histories in the cloud.

Step 1 of 1

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.

Step 1 of 1

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

See Azure Machine Learning pricing

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

"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

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"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

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"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."

Dr. Deepa Kasinathan, Product Manager and Group Leader, Robotron Datenbank-Software GmbH

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"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."

Ignasi Paredes-Oliva, Lead Data Scientist, Nestlé Global Security Operations Center

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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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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.

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