设置在团队数据科学过程中使用的数据科学环境Set up data science environments for use in the Team Data Science Process
团队数据科学过程使用各种数据科学环境来完成数据的存储、处理和分析。The Team Data Science Process uses various data science environments for the storage, processing, and analysis of data. 它们包括 Azure Blob 存储、几种类型的 Azure 虚拟机、HDInsight (Hadoop) 群集和 Azure 机器学习工作区。They include Azure Blob Storage, several types of Azure virtual machines, HDInsight (Hadoop) clusters, and Azure Machine Learning workspaces. 关于要使用的环境类型的决策具体取决于要建模的数据类型和数量以及云中数据的目标目的地。The decision about which environment to use depends on the type and quantity of data to be modeled and the target destination for that data in the cloud.
- 有关做出此决策时要考虑的问题的指南，请参阅规划 Azure 机器学习数据科学环境。For guidance on questions to consider when making this decision, see Plan Your Azure Machine Learning Data Science Environment.
- 有关执行高级分析时可能遇到的某些方案的目录，请参阅团队数据科学过程的方案For a catalog of some of the scenarios you might encounter when doing advanced analytics, see Scenarios for the Team Data Science Process
以下文章介绍如何设置团队数据科学过程使用的各种数据科学环境。The following articles describe how to set up the various data science environments used by the Team Data Science Process.
- Azure 存储帐户Azure storage-account
- HDInsight (Hadoop) 群集HDInsight (Hadoop) cluster
- Azure 机器学习工作室(经典)工作区Azure Machine Learning Studio (classic) workspace
Microsoft 数据科学虚拟机 (DSVM) 同时也可用作 Azure 虚拟机 (VM) 映像。The Microsoft Data Science Virtual Machine (DSVM) is also available as an Azure virtual machine (VM) image. 此 VM 使用几种常用工具（通常用于数据分析和机器学习）进行预安装和配置。This VM is pre-installed and configured with several popular tools that are commonly used for data analytics and machine learning. Windows 和 Linux 上均可使用 DSVM。The DSVM is available on both Windows and Linux. 有关详细信息，请参阅基于云的 Data Science Virtual Machine for Linux and Windows 简介。For more information, see Introduction to the cloud-based Data Science Virtual Machine for Linux and Windows.
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