Azure Machine Learning


エンド ツー エンドの機械学習ライフサイクルを加速させる

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.

エンドツーエンドの MLOps

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.


オープンソース フレームワークと MLflow、Kubeflow、ONNX、PyTorch、TensorFlow、Python、R などの言語に対する最高レベルのサポートを受けることができます。

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.

MLOps を使用して大規模な運用を実現する

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.


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.


  • 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.
  • ポリシー、監査証跡、クォータ、コスト管理を使用してガバナンスを管理できます。
  • FedRAMP High や DISA IL5 などの 60 の認定を含む包括的なポートフォリオを使用して、コンプライアンスを効率化できます。



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.




中央レジストリを使用して、データ、モデル、メタデータを格納および追跡できます。系列とガバナンス データを自動的にキャプチャできます。Git を使用して作業を追跡し、GitHub Actions を使用してワークフローを実装できます。実行を管理および監視したり、トレーニングと実験のために複数の実行を比較したりすることができます。

自動スケール コンピューティング

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).

その他の Azure サービスとの緊密な統合

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.


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

Responsible machine learning


エンタープライズ グレードのセキュリティ

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.

Cost management

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


See Azure Machine Learning pricing.

Azure Machine Learning の習得

TensorFlow、Spark、Kubernetes を使用して Azure で自動化されスケーラブルなエンドツーエンドの機械学習モデルとパイプラインを構築するための専門的なテクニックを習得する

Packt:データ サイエンスの原則

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 のリーダー 2020 年

Forrester は、Microsoft と Azure Machine Learning を The Forrester Wave™: ノートブック ベースの予測分析と機械学習、2020 年第 3 四半期のリーダーに位置付けています。

Azure Machine Learning を使用する方法

スタジオ Web エクスペリエンスに移動



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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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今すぐ Azure Machine Learning を始めましょう

無料の Azure アカウントにサインアップすることで、すぐにアクセスできるようになり、$200 クレジットを獲得できます。

Azure portal にサインインします。


Azure Machine Learning をご利用のお客様

"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 氏: BI および分析ディレクター、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 氏 (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 氏: 分析および AI リーダー、Partner and Advisory Data、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

Azure Machine Learning の更新プログラム、ブログ、お知らせ

Azure Machine Learning に関してよく寄せられる質問

  • このサービスは複数の国/地域で一般提供されており、今後も増える予定です。
  • Azure Machine Learning のサービス レベル アグリーメント (SLA) は 99.9% です。
  • Azure Machine Learning スタジオは、機械学習サービスの最上位レベルのリソースです。ここは、データ サイエンティストや開発者が、機械学習モデルを構築、トレーニング、デプロイするためのすべての成果物を操作するための中心の場所です。

準備が整ったら、Azure の無料アカウントを設定しましょう。