This article is Request! Tips for developing on Azure using Python![PR] This is his 4th day of Microsoft Japan Advent Calendar 2020 (I will write it later).
Data Science Virtual Machines is the best environment for data analysis from now on! It is a story.
The reason is as follows.
Azure Machine Learning is the best environment for machine learning from now on! , I will explain the difference between Azure Machine Learning and Data Science Virtual Machines.
The difference is that Data Science Virtual Machines (https://azure.microsoft.com/ja-jp/services/virtual-machines/data-science-virtual-machines/?WT.mc_id=AZ-MVP-5001601) is a customized VM image for data science, while Azure Machine Learning (https://azure.microsoft.com/ja-jp/services/machine-learning/?WT.mc_id=AZ-MVP-5001601) is a fully managed service.
Other languages supported by Azure Machine Learning are Python and R, but in Data Science Virtual Machines, besides Python and R, Julia, SQL, C #, Java, Node.js , Supports F #.
In this way, the uses are fundamentally different. It's not a fully managed service, so it's clearly more customizable with Data Science Virtual Machines (https://azure.microsoft.com/ja-jp/services/virtual-machines/data-science-virtual-machines/?WT.mc_id=AZ-MVP-5001601).
However, depending on the requirements, the functionality may be fat, so there is no such thing as which is better and which is worse. I think it is important to use the service that meets your requirements.
** Day 5 is @ changeworld's "Deploy the strongest front-end Streamlit for data scientists on Azure!". Please continue to enjoy. ** **
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