通过试验和模型管理，使用端到端、可缩放的受信平台，让所有人都能够获得 AI 体验
Empower data scientists and developers with a wide range of productive experiences to build, train, and deploy machine learning models and foster team collaboration. Accelerate time to market with industry-leading MLOps—machine learning operations, or DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning.
Robust MLOps capabilities that enable creation and deployments of models at scale using automated and reproducible machine learning workflows
Rich set of built-in responsible capabilities to understand, protect, and control data, models, and processes
Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R
使用 MLOps 执行大规模操作
Take advantage of MLOps to streamline the machine learning lifecycle, from building models to deployment and management. Create reproducible workflows with machine learning pipelines, and train, validate, and deploy thousands of models at scale, from the cloud to the edge. Use managed online and batch endpoints to seamlessly deploy and score models without managing the underlying infrastructure. Use Azure DevOps or GitHub Actions to schedule, manage, and automate the machine learning pipelines, and use advanced data-drift analysis to improve model performance over time.
Access state-of-the-art responsible machine learning capabilities to understand, control, and help protect 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, Visual Studio Code, Jupyter Notebooks, and CLIs, or languages such as Python and R. Use ONNX Runtime to optimize and accelerate inferencing across cloud and edge devices. Track all aspects of your training experiments using MLflow.
- 在混合基础结构中，借助 Azure Arc 互操作性在本地、多云环境和边缘的 Kubernetes 群集上训练模型。
- 通过 60 项认证（包括 FedRAMP High 和 DISA IL5）的综合产品组合，优化合规性。
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.
Use the central registry to store and track data, models, and metadata. Automatically capture lineage and governance data. Use Git to track work and GitHub Actions to implement workflows. Manage and monitor runs, or compare multiple runs for training and experimentation. Use managed endpoints to operationalize model deployment and scoring, log metrics, and perform safe model rollouts.
使用托管计算来分发训练并快速测试、验证和部署模型。在工作区内共享 CPU 和 GPU 群集并自动缩放，以满足机器学习需求。
与其他 Azure 服务深度集成
借助与 Microsoft Power BI 和 Azure 服务（例如 Azure Synapse Analytics、Azure 认知搜索、Azure 数据工厂、Azure Data Lake、Azure Arc 和 Azure Databricks）的内置集成来提高工作效率。
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.
Get model transparency at training and inferencing with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. Help protect data with differential privacy and confidential machine learning pipelines.
使用工作区和资源级别配额限制，更好地管理 Azure 机器学习计算实例的资源分配。
查看 Azure 机器学习定价
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客户使用 Azure 机器学习
Jolie Vitale，Carhartt 公司的 BI 和分析主管
"The model we deployed on Azure Machine Learning helped us choose three new retail locations. Those stores exceeded their revenue plans by over 200 percent [that] December, the height of our season, and within months of opening were among the best-performing stores in their districts."
"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."
Joey Chua，AGL 机器学习工程高级经理
"Azure Machine Learning allows us to manage the entire lifecycle, from experimentation and development to production and enhancements."
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."
Alex Mohelsky，EY Canada 合作伙伴兼顾问、数据分析与 AI 负责人
"We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators."