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什么是 Azure 机器学习服务?What is Azure Machine Learning service?

Azure 机器学习服务是一项云服务,可以使用它来训练、部署、自动执行以及管理机器学习模型,所有这些都是在云提供的广泛范围内进行的。Azure Machine Learning service is a cloud service that you use to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides.

什么是机器学习?What is machine learning?

机器学习是一项数据科研技术,可以让计算机根据现有的数据来预测将来的行为、结果和趋势。Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. 使用机器学习,计算机可以在不需显式编程的情况下进行学习。By using machine learning, computers learn without being explicitly programmed.

机器学习的预测可让应用和设备变得更聪明。Forecasts or predictions from machine learning can make apps and devices smarter. 例如,在网上购物时,机器学习可根据购买的产品帮助推荐其他产品。For example, when you shop online, machine learning helps recommend other products you might want based on what you've bought. 或者,在刷信用卡时,机器学习可将这笔交易与交易数据库进行比较,帮助检测诈骗。Or when your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud. 当吸尘器机器人打扫房间时,机器学习可帮助它确定作业是否已完成。And when your robot vacuum cleaner vacuums a room, machine learning helps it decide whether the job is done.

什么是 Azure 机器学习服务?What is Azure Machine Learning service?

Azure 机器学习服务提供了一个基于云的环境,你可以使用这一环境来准备数据、培训、测试、部署、管理和跟踪机器学习模型。Azure Machine Learning service provides a cloud-based environment you can use to prep data, train, test, deploy, manage, and track machine learning models. 开始在本地计算机上训练,然后横向扩展到云。Start training on your local machine and then scale out to the cloud. 此服务完全支持开源技术(例如 PyTorch、TensorFlow 和 scikit-learn),可以用于任何类型的机器学习,从经典机器学习到深度学习、监督式学习和非监督式学习,不一而足。The service fully supports open-source technologies such as PyTorch, TensorFlow, and scikit-learn and can be used for any kind of machine learning, from classical ml to deep learning, supervised and unsupervised learning.

使用如下所示的丰富工具浏览并准备数据、训练并测试模型,以及对其进行部署:Explore and prepare data, train and test models, and deploy them using rich tools such as:

通过 Azure 机器学习服务,我可以执行哪些操作?What can I do with Azure Machine Learning service?

Azure 机器学习 Python SDK 与开源 Python 包配合使用,或者使用可视界面(预览版),你自己可以在 Azure 机器学习服务工作区中生成并训练高度精确的机器学习和深度学习模型。Use the Azure Machine Learning Python SDK with open-source Python packages, or use the visual interface (preview) to build and train highly accurate machine learning and deep-learning models yourself in an Azure Machine Learning service Workspace.

可以从开源 Python 包中提供的许多机器学习组件(例如 Scikit-learnTensorflowPyTorchMXNet)中进行选择。You can choose from many machine learning components available in open-source Python packages, such as Scikit-learn, Tensorflow, PyTorch, and MXNet.

不管是编写代码还是使用可视界面,都可以在试验时跟踪多个运行,以便查找最佳解决方案并管理部署的模型。Whether you write code or use the visual interface, you can track multiple runs as you experiment to find the best solution as well as manage the deployed models.

代码优先体验Code-first experience

开始使用 Azure 机器学习 Python SDK 在本地计算机上训练,然后横向扩展到运行。Start training on your local machine using the Azure Machine Learning Python SDK and then scale out to the cloud. 借助许多可用的计算目标(例如 Azure 机器学习计算和 Azure Databricks)以及高级超参数优化服务,可以利用云的强大功能更快地生成更好的模型。With many available compute targets, like Azure Machine Learning Compute and Azure Databricks, and with advanced hyperparameter tuning services, you can build better models faster by using the power of the cloud.

也可使用 SDK 自动完成模型训练和优化You can also automate model training and tuning using the SDK.

基于 UI 的低代码体验UI-based, low-code experience

若要进行无代码训练,请尝试:For code-free training, try:

操作化 (MLOps)Operationalization (MLOps)

有了正确的模型以后,即可轻松地将其用在 Web 服务中、IoT 设备上或 Power BI 中。When you have the right model, you can easily use it in a web service, on an IoT device, or from Power BI. 有关详细信息,请参阅有关部署方式及位置的文章。For more information, see the article on how to deploy and where.

然后,可以使用适用于 Python 的 Azure 机器学习 SDKAzure 门户来管理已部署的模型。Then you can manage your deployed models by using the Azure Machine Learning SDK for Python or the Azure portal.

可以使用这些模型实时返回预测,或者在有大量数据的情况下异步返回预测。These models can be consumed and return predictions in real time or asynchronously on large quantities of data.

使用高级机器学习管道,可以在每一步(从数据准备、模型训练和评估一直到部署)进行协作。And with advanced machine learning pipelines, you can collaborate on each step from data preparation, model training and evaluation, through deployment. 使用 Pipelines 可以:Pipelines allow you to:

  • 自动完成云中的端到端机器学习过程Automate the end-to-end machine learning process in the cloud
  • 重用组件,并仅在需要时重新运行步骤Reuse components and only re-run steps when needed
  • 在每个步骤中使用不同的计算资源Use different compute resources in each step
  • 运行批量评分任务Run batch scoring tasks

若要开始使用 Azure 机器学习服务,请参阅后续步骤To get started using Azure Machine Learning service, see Next steps.

Azure 机器学习服务与工作室有何不同?How does Azure Machine Learning service differ from Studio?

机器学习工作室是一个协作型拖放式可视工作区,可以在其中生成、测试和部署机器学习解决方案,不需编写代码。Machine Learning Studio is a collaborative, drag-and-drop visual workspace where you can build, test, and deploy machine learning solutions without needing to write code. 它使用预先生成和预先配置的机器学习算法、数据处理模块和专用计算平台。It uses prebuilt and preconfigured machine learning algorithms and data-handling modules as well as a proprietary compute platform.

Azure 机器学习服务提供 SDK 可视界面(预览版),可以快速准备数据以及训练和部署机器学习模型。Azure Machine Learning service provides both SDKs -and- a visual interface(preview), to quickly prep data, train and deploy machine learning models. 此可视界面(预览版)提供与工作室类似的拖放体验。This visual interface (preview) provides a similar drag-and-drop experience to Studio. 但是,不像工作室的专用计算平台,此可视界面使用你自己的计算资源,并且已完全集成到 Azure 机器学习服务中。However, unlike the proprietary compute platform of Studio, the visual interface uses your own compute resources and is fully integrated into Azure Machine Learning service.

这是一个快速比较。Here is a quick comparison.

机器学习工作室Machine Learning Studio Azure 机器学习服务:Azure Machine Learning service:
可视界面Visual interface
正式发布 (GA)Generally available (GA) 预览In preview
界面的模块Modules for interface 很多Many 常用模块的初始集Initial set of popular modules
训练计算目标Training compute targets 专用计算目标,仅限 CPU 支持Proprietary compute target, CPU support only 支持 Azure 机器学习计算、GPU 或 CPU。Supports Azure Machine Learning compute, GPU or CPU.
(其他在 SDK 中受支持的计算)(Other computes supported in SDK)
部署计算目标Deployment compute targets 专用 Web 服务格式,不可自定义Proprietary web service format, not customizable 企业安全选项和 Azure Kubernetes 服务。Enterprise security options & Azure Kubernetes Service.
(SDK 中支持的其他计算(Other computes supported in SDK)
自动化模型训练和超参数优化Automated model training and hyperparameter tuning No 在可视界面中尚不支持。Not yet in visual interface.
(在 SDK 和 Azure 门户中受支持。)(Supported in the SDK and Azure portal.)

参考以下教程试用可视界面(预览版):教程:使用可视界面预测汽车价格Try out the visual interface (preview) with Tutorial: Predict automobile price with the visual interface.

备注

在工作室中创建的模型不能通过 Azure 机器学习服务来部署或管理。Models created in Studio can't be deployed or managed by Azure Machine Learning service. 但是,在服务可视界面中创建和部署的模型可以通过 Azure 机器学习服务工作区进行管理。However, models created and deployed in the service visual interface can be managed through the Azure Machine Learning service workspace.

免费试用Free trial

如果还没有 Azure 订阅,请在开始前创建免费帐户。If you don’t have an Azure subscription, create a free account before you begin. 立即试用 Azure 机器学习服务免费版或付费版Try the free or paid version of Azure Machine Learning service today.

你将获得可用于 Azure 服务的额度。You get credits to spend on Azure services. 信用额度用完后,可以保留该帐户并继续使用免费的 Azure 服务After they're used up, you can keep the account and use free Azure services. 除非显式更改设置并要求付费,否则不会对信用卡收取任何费用。Your credit card is never charged unless you explicitly change your settings and ask to be charged. 或者激活 MSDN 订户权益,享受每月试用付费版 Azure 服务的信用额度。Or activate MSDN subscriber benefits, which give you credits every month that you can use for paid Azure services.

后续步骤Next steps