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. 无论你是希望使用 SDK 编写 Python 或 R 代码,还是在工作室中使用无代码/低代码选项,你都可以在 Azure 机器学习工作区中构建、训练和跟踪机器学习和深度学习模型。Whether you prefer to write Python or R code with the SDK or work with no-code/low-code options in the studio, you can build, train, and track 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.

此服务还与常用的深度学习和强化学习开放源代码工具(如 PyTorch、TensorFlow、scikit-learn 和 Ray RLlib)进行互操作。The service also interoperates with popular deep learning and reinforcement open-source tools such as PyTorch, TensorFlow, scikit-learn, and Ray RLlib.

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.

在工作室中生成 ML 模型Build ML models in the studio

Azure 机器学习工作室是 Azure 机器学习中的 Web 门户,提供用于模型训练、部署和资产管理的低代码和无代码选项。Azure Machine Learning studio is a web portal in Azure Machine Learning for low-code and no-code options for model training, deployment, and asset management. 工作室与 Azure 机器学习 SDK 集成,以实现无缝体验。The studio integrates with the Azure Machine Learning SDK for a seamless experience. 有关详细信息,请参阅什么是 Azure 机器学习工作室For more information, see What is Azure Machine Learning studio.

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.

后续步骤Next steps