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Azure Machine Learning

Enterprise-grade machine learning service to build and deploy models faster

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.

Operationalise 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 per cent fewer steps for training models

90 per cent 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

Data labeling

Label training data and manage labeling projects.

Data preparation

Integrate 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 ML

Automatically train and tune accurate models.

Drag-and-drop designer

Design with drag-and-drop development interface.

Experiments

Run experiments and create and share custom dashboards.

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

Utilize one-click deployment for batch and real-time inference.

Pipelines and CI/CD

Automate machine learning workflows.

Pre-built 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.

Optimize models

Accelerate training and inference and lower costs with ONNX Runtime.

Monitor and analyze

Track, log, and analyze data, models, and resources.

Data drift

Detect drift and maintain model accuracy.

Error analysis

Debug models and optimize model accuracy.

Audit

Trace ML artifacts for compliance.

Policies

Leverage built-in and custom policies for compliance management.

Security

Enjoy continuous monitoring with Azure Security Center.

Control costs

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.

Operationalise 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 visualisations 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

Data labelling

Create, manage and monitor labelling projects and automate iterative tasks with machine learning-assisted labelling.

Data preparation

Perform interactive data preparation with PySpark, using built-in integration with Azure Synapse Analytics.

Collaborative notebooks

Maximise 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 such as 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 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.

Managed endpoints

Use managed endpoints to operationalise model deployment and scoring, log metrics and perform safe model roll-outs.

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.

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.

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

Enable IT to reduce cost and better manage resource allocations for compute instances with workspace and resource-level quota limits and automatic shutdown.

Only pay for what you need, with no upfront cost

Mastering Azure Machine Learning

Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark and Kubernetes.

Principles of Data Science

Many people working with data have developed skills in math, programming or domain expertise, but proper data science calls for all three. This comprehensive e-book helps fill in the gaps.

The Forrester WaveTM 2020

Forrester names Microsoft Azure Machine Learning a leader in The Forrester Wave™: Notebook-based predictive analytics and machine learning, Q3 2020.

Projected ROI range up to 3 times higher – Forrester Total Economic ImpactTM (TEI)

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 organisations.

Machine learning solutions with enterprise scale whitepaper

Empowering organisations to build secure, scalable and equitable ML solutions with Azure Machine Learning.

Responsible AI whitepaper

Tools and methods to understand, protect and control your models.

Machine learning operations (MLOps) whitepaper

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

Step 1 of 1

Author new models and store your compute targets, models, deployments and 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.

Start using Azure Machine Learning today

Get instant access and a $200 credit by signing up for an Azure free account.

Sign in to the Azure portal.

Customers using Azure Machine Learning

"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
FedEx

"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
BMW Group

"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
Nestle Italia

"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
PepsiCo

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.

DB Systel GmbH

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 per cent uptime.
  • Azure Machine Learning studio is the top-level resource for Machine Learning. This capability provides a centralised place for data scientists and developers to work with all the artifacts for building, training and deploying machine learning models.

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