| title | description | services | documentationcenter | author | manager | editor | ms.assetid | ms.service | ms.workload | ms.tgt_pltfrm | ms.devlang | ms.topic | ms.date | ms.author |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Set up data science environments for use in the Team Data Science Process | Microsoft Docs |
Set up data science environments for use in the Team Data Science Process |
machine-learning |
bradsev |
jhubbard |
cgronlun |
481cfa6a-7ea3-46ac-b0f9-2e3982c37153 |
machine-learning |
data-services |
na |
na |
article |
11/01/2016 |
bradsev |
The Team Data Science Process uses various data science environments for the storage, processing, and analysis of data. They include Azure Blob Storage, several types of Azure virtual machines, HDInsight (Hadoop) clusters, and Azure Machine Learning workspaces. 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.
- For guidance on questions to consider when making this decision, see Plan Your Azure Machine Learning Data Science Environment.
- For a catalog of some of the scenarios you might encounter when doing advanced analytics, see Scenarios for the Team Data Science Process
This menu links to topics that describe how to set up the various data science environments used by the Team Data Science Process.
[!INCLUDE data-science-environment-setup]
The Microsoft Data Science Virtual Machine (DSVM) is also available as an Azure virtual machine (VM) image. This VM is pre-installed and configured with several popular tools that are commonly used for data analytics and machine learning. The DSVM is available on both Windows and Linux. For further information, see Introduction to the cloud-based Data Science Virtual Machine for Linux and Windows.