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什么是 Azure 机器学习?What is Azure Machine Learning?

在本文中,你将了解 Azure 机器学习,这是一种基于云的环境,你可以使用它来训练、部署、自动化、管理和跟踪 ML 模型。In this article, you learn about Azure Machine Learning, a cloud-based environment you can use to train, deploy, automate, manage, and track ML models.

Azure 机器学习可用于任何类型的机器学习,从传统 ml 到深度学习、监督式和非监督式学习。Azure Machine Learning can be used for any kind of machine learning, from classical ml to deep learning, supervised, and unsupervised learning. 无论你是否希望编写 Python 或 R 代码或零代码/低代码选项(如设计器),你都可以在 Azure 机器学习工作区中构建、训练和跟踪非常准确的机器学习和深度学习模型。Whether you prefer to write Python or R code or zero-code/low-code options such as the designer, you can build, train, and track highly accurate machine learning and deep-learning models in an Azure Machine Learning Workspace.

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.

该服务还可与常用的开源工具(如 PyTorch、TensorFlow 和 scikit-learn)进行互操作。The service also interoperates with popular open-source tools, such as PyTorch, TensorFlow, and scikit-learn.


免费试用!Free trial! 如果没有 Azure 订阅,请在开始之前创建一个免费帐户。If you don’t have an Azure subscription, create a free account before you begin. 立即试用免费版或付费版 Azure 机器学习Try the free or paid version of Azure Machine Learning today. 你将获得可用于 Azure 服务的赠送额度。You get credits to spend on Azure services. 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.

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.

适用于每个任务的机器学习工具Machine learning tools to fit each task

Azure 机器学习为其机器学习工作流提供了开发人员和数据科学家所需的所有工具,包括:Azure Machine Learning provides all the tools developers and data scientists need for their machine learning workflows, including:

甚至可以使用 MLflow 跟踪指标并部署模型或使用 Kubeflow 生成端到端工作流管道You can even use MLflow to track metrics and deploy models or Kubeflow to build end-to-end workflow pipelines.

在 Python 或 R 中生成 ML 模型Build ML models in Python or R

开始使用 Azure 机器学习 Python SDKR SDK 在本地计算机上训练。Start training on your local machine using the Azure Machine Learning Python SDK or R SDK. 然后,横向扩展到云。Then, you can 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.

使用无代码工具生成 ML 模型Build ML models with no-code tools

对于无代码或低代码训练和部署,请尝试:For code-free or low-code training and deployment, try:

  • Azure 机器学习设计器(预览版)Azure Machine Learning designer (preview)

    使用设计器可在不编写任何代码的情况下准备数据、训练、测试、部署、管理和跟踪机器学习模型。Use the designer to prep data, train, test, deploy, manage, and track machine learning models without writing any code. 不需要编程,只需以可视方式连接数据集和模块即可构建模型。There is no programming required, you visually connect datasets and modules to construct your model. 尝试设计器教程Try out the designer tutorial.

    有关详细信息,请参阅 Azure 机器学习设计器概述文章Learn more in the Azure Machine Learning designer overview article.

    Azure 机器学习设计器示例

  • 自动化机器学习 UIAutomated machine learning UI

    了解如何在易于使用的界面中创建自动化 ML 试验Learn how to create automated ML experiments in the easy-to-use interface.

    Azure 机器学习工作室导航窗格Azure Machine Learning studio navigation pane

MLOps:部署和生命周期管理MLOps: Deploy & lifecycle management

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 机器学习工作室机器学习 CLI 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, Azure Machine Learning studio, or the machine learning CLI.

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 rerun 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

如果要使用脚本自动执行机器学习工作流,机器学习 CLI 提供了执行常见任务(如提交训练运行或部署模型)的命令行工具。If you want to use scripts to automate your machine learning workflow, the machine learning CLI provides command-line tools that perform common tasks, such as submitting a training run or deploying a model.

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

与其他服务集成Integration with other services

Azure 机器学习可与 Azure 平台上的其他服务配合使用,还能与诸如 Git 和 MLFlow 之类的开源工具集成。Azure Machine Learning works with other services on the Azure platform, and also integrates with open source tools such as Git and MLFlow.

安全通信Secure communications

Azure 存储帐户、计算目标和其他资源可在虚拟网络内安全地用于定型模型并执行推理。Your Azure Storage account, compute targets, and other resources can be used securely inside a virtual network to train models and perform inference. 有关详细信息,请参阅虚拟网络中的安全试验和推理For more information, see Secure experimentation and inference in a virtual network.

Basic 和 Enterprise EditionBasic & Enterprise editions

Azure 机器学习提供了两个版本,专为你的机器学习需求提供:Azure Machine Learning offers two editions tailored for your machine learning needs:

  • Basic(正式版)Basic (generally available)
  • Enterprise(预览版)Enterprise (preview)

这些版本确定开发人员和数据科学家的工作区中可用的机器学习工具。These editions determine which machine learning tools are available to developers and data scientists from their workspace.

Basic 工作区允许继续使用 Azure 机器学习,并只为在机器学习过程中使用的 Azure 资源付费。Basic workspaces allow you to continue using Azure Machine Learning and pay for only the Azure resources consumed during the machine learning process. Enterprise Edition 工作区只对其 Azure 使用情况收费,它是预览版。Enterprise edition workspaces will be charged only for their Azure consumption while the edition is in preview. 若要详细了解 Azure 机器学习中提供的内容,请参阅版本概述和定价页Learn more about what's available in the Azure Machine Learning edition overview & pricing page.

你始终可以在创建工作区时分配版本。You assign the edition whenever you create a workspace. 而且,预先存在的工作区已转换为 Basic Edition。And, pre-existing workspaces have been converted to the Basic edition for you. Basic Edition 包括已于 2019 年 10 月公开发布的所有功能。Basic edition includes all features that were already generally available as of October 2019. 使用 Enterprise Edition 功能构建的工作区中的任何试验都将以只读模式继续供你使用,直至升级到 Enterprise。Any experiments in those workspaces that were built using Enterprise edition features will continue to be available to you in read-only until you upgrade to Enterprise. 了解如何将 Basic 工作区升级到 Enterprise EditionLearn how to upgrade a Basic workspace to Enterprise edition.

客户负责在此期间因计算和其他 Azure 资源产生的成本。Customers are responsible for costs incurred on compute and other Azure resources during this time.

后续步骤Next steps