Azure Machine Learning

Machine-Learning-Dienst für Unternehmen zur schnelleren Erstellung und Bereitstellung von Modellen

Beschleunigter vollständiger Lebenszyklus mit maschinellem Lernen

The Azure Machine Learning service empowers developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning.

Machine learning for all skills

Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning.


Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete machine learning lifecycle.

State-of-the-art responsible machine learning

Responsible machine learning capabilities—understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the machine learning lifecycle with audit trials and datasheets.

Offen und interoperabel

Optimale Unterstützung von Open-Source-Frameworks und Sprachen wie MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python und R

Boost productivity with machine learning for all skills

Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level. Use the no-code designer to get started with visual machine learning or built-in collaborative Jupyter Notebooks for a code-first experience. Accelerate model creation with automated machine learning, and access built-in feature engineering, algorithm selection, and hyperparameter sweeping to develop highly accurate models.

Operationalisierung nach Maß mit MLOps

MLOps, or DevOps for machine learning, streamlines the machine learning lifecycle, from building models to deployment and management. Use machine learning pipelines to build repeatable workflows, and use a rich model registry to track your assets. Manage production workflows at scale using advanced alerts and machine learning automation capabilities. Profile, validate, and deploy machine learning models anywhere, from the cloud to the edge, to manage production machine learning workflows at scale in an enterprise-ready fashion.

Build responsible machine learning solutions

Access state-of-the-art responsible machine learning capabilities to understand, protect, and control your data, models, and processes. Explain model behavior during training and inferencing, and build for fairness by detecting and mitigating model bias. Preserve data privacy throughout the machine learning lifecycle with differential privacy techniques, and use confidential computing to secure machine learning assets. Automatically maintain audit trails, track lineage, and use model datasheets to enable accountability.

Innovationen mit einer offenen und flexiblen Plattform schaffen

Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow, or scikit-learn, or the open and interoperable ONNX format. Choose the development tools that best meet your needs, including popular IDEs, Jupyter Notebooks, and CLIs, or languages such as Python and R. Use ONNX Runtime to optimize and accelerate inferencing across cloud and edge devices.

Erweiterte Sicherheit und Governance

  • Get end-to-end security and build on the trusted cloud with Azure.
  • Protect your resources with granular role-based access, custom roles, and built-in mechanisms for identity authentication.
  • Build, train, and deploy models more securely by isolating your network with virtual networks and private links.
  • Verwalten Sie Governance mit Richtlinien, Audit-Trails sowie Kontingent- und Kostenverwaltung.
  • Optimieren Sie die Compliance mit einem umfassenden Portfolio, das 60 Zertifizierungen enthält, darunter auch FedRAMP High und DISA IL5.

Wichtige Dienstfunktionen

Kollaborative Notebooks

Maximize productivity with IntelliSense, easy compute and kernel switching, and offline notebook editing.

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 with modules for data transformation, model training, and evaluation, or to easily create and publish machine learning pipelines.


Nutzen Sie das durch maschinelles Lernen unterstützte Beschriftungsfeature, um schnell Daten vorzubereiten, Beschriftungsprojekte zu verwalten und zu überwachen sowie um iterative Aufgaben zu automatisieren.


Nutzen Sie die zentrale Registrierung, um Daten, Modelle und Metadaten zu speichern und nachzuverfolgen. Erfassen Sie Daten zur Herkunft und Governance automatisch. Verwenden Sie Git zum Nachverfolgen von Arbeit, und nutzen Sie GitHub Actions zum Implementieren von Workflows. Verwalten und überwachen Sie Ausführungen, oder vergleichen Sie mehrere Ausführungen für Training und Experimente.

Automatische Skalierung von Computeressourcen

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.

RStudio support

Build and deploy models and monitor runs with built-in R support and RStudio Server (open source edition).

Enge Verzahnung mit anderen Azure-Diensten

Accelerate productivity with built-in integration with Microsoft Power BI and Azure services such as Azure Synapse Analytics, Azure Cognitive Search, Azure Data Factory, Azure Data Lake, and Azure Databricks.

Vertiefendes Lernen

Scale reinforcement learning to powerful compute clusters, support multi-agent scenarios, and access open-source reinforcement learning algorithms, frameworks, and environments.

Responsible machine learning

Verschaffen Sie sich mithilfe der Interpretationsfunktionen Modelltransparenz beim Training und Herleiten von Rückschlüssen. Bewerten Sie die Modellfairness, und vermeiden Sie Unfairness mithilfe von Ungleichheitsmetriken. Schützen Sie Daten mit Differential Privacy.


Build and deploy models more securely with network isolation and private link capabilities, role-based access control for resources and actions, custom roles, and managed identity for compute resources.


Better manage resource allocations for Azure Machine Learning compute instances with workspace- and resource-level quota limits.

Bezahlen Sie nutzungsbasiert – ohne Vorabkosten

See Azure Machine Learning pricing.

Azure Machine Learning meistern

Eignen Sie sich professionelle Methoden für die Entwicklung automatisierter und hochgradig skalierbarer Machine Learning-Modelle und -Pipelines mithilfe von TensorFlow, Spark und Kubernetes an.

Packt: Prinzipien der 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.

Forrester Wave Leader 2020

Forrester bezeichnet Microsoft und Azure Machine Learning in The Forrester Wave™ als führend: Notebook-based Predictive Analytics and Machine Learning (Notebook-basiertes Herangehen an Predictive Analytics und Machine Learning), Q3 2020

Azure Machine Learning verwenden

Machine Learning Studio-Webfunktionen aufrufen

Erstellen und Trainieren

Bereitstellen und Verwalten

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

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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.

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Stellen Sie Ihr Machine Learning-Modell in der Cloud oder am Edge bereit, überwachen Sie die Leistung, und trainieren Sie nach Bedarf erneut.

Jetzt mit Azure Machine Learning durchstarten

Wenn Sie sich für ein kostenloses Azure-Konto registrieren, erhalten Sie sofortigen Zugriff und ein Guthaben von $200.

Melden Sie sich beim Azure-Portal an.

Kunden, die Azure Machine Learning nutzen

"The model we deployed on Azure Machine Learning helped us choose the three new retail locations we opened in 2019. Those stores exceeded their revenue plans by over 200 percent in December, the height of our season, and within months of opening were among the best-performing stores in their districts."

Jolie Vitale: Director of BI and Analytics, Carhartt

By using Azure Machine Learning, Scandinavian Airlines (SAS) is accurately identifying fraud with proficiency that wasn’t possible through manual methods. In the case of retroactively registering a flight for EuroBonus miles—a common source of fraud—the new system predicts fraud with 99 percent accuracy.

Scandinavian Airlines

"If I have 200 models to train—I can just do this all at once. It can be farmed out to a huge compute cluster, and it can be done in minutes. So I'm not waiting for days."

Dean Riddlesden, Senior Data Scientist, Global Analytics, Walgreens Boots Alliance
Walgreens Boots Alliance

"We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators."

Alex Mohelsky: Partner and Advisory Data, Analytic, and AI Leader, EY Canada

"The automated machine learning capabilities in Azure Machine Learning save our data scientists from doing a lot of time-consuming work, which reduces our time to build models from several weeks to a few hours."

Xiaodong Wang, CEO, TalentCloud

Updates, Blogs und Ankündigungen zu Azure Machine Learning

Häufig gestellte Fragen zu Azure Machine Learning

  • Der Dienst ist in mehreren Ländern/Regionen allgemein verfügbar – weitere werden in Kürze folgen.
  • In der Vereinbarung zum Servicelevel (SLA) für Azure Machine Learning wird eine Verfügbarkeit von 99,9 % garantiert.
  • Azure Machine Learning Studio ist die wichtigste Ressource für den Machine Learning-Dienst. Sie stellt eine zentrale Anlaufstelle für Data Scientists und Entwickler bereit, über die alle Artefakte zum Erstellen, Trainieren und Bereitstellen von Machine Learning-Modellen genutzt werden können.

Sind Sie bereit? Dann richten Sie Ihr kostenloses Azure-Konto ein.