问题描述
我是Azure ML的新手。我有一些疑问。请谁能澄清下面列出的我的疑问。
I am new to Azure ML. I am having some doubts .Could anyone please clarify my doubts listed below.
- Azure ML服务Azure ML实验服务之间有什么区别？
- Azure ML工作台和Azure ML Studio有什么区别。
- 我想使用Azure ML实验服务来构建少量模型和创建网站蜜蜂。是否可以使用ML studio做同样的事情。
- 还有ML实验服务还要求我安装用于创建Web服务的Windows泊坞窗。
我可以在不使用docker的情况下创建Web服务吗？
- What is the difference between Azure ML service Azure ML experimentation service.
- What is the difference between Azure ML workbench and Azure ML Studio.
- I want to use azure ML Experimentation service for building few models and creating web API's. Is it possible to do the same with ML studio.
- And also ML Experimentation service requires me to have a docker for windows installed for creating web services.Can i create web services without using docker?
推荐答案
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AML实验是我们许多新的ML产品之一，包括数据准备，实验，模型管理和可操作性。 Workbench是PREVIEW产品，它为其中一些服务提供GUI。但这只是运行所需的CLI的安装程序/包装程序。这些服务基于Spark和Python。其他Python框架也可以使用，并且您可以从Python调用Java / Scala有点技巧。不太确定 Azure ML服务是什么意思，也许您是指我上面提到的可操作化服务。这将使您能够快速使用Docker容器创建基于Python的新API，并与模型管理帐户建立联系，以跟踪模型与服务之间的关联。此处的所有服务仍处于预览状态，并可能在GA发布之前中断更改。
The AML Experimentation is one of our many new ML offerings, including data preparation, experimentation, model management, and operationalization. Workbench is a PREVIEW product that provides a GUI for some of these services. But it is just a installer/wrapper for the CLI that is needed to run. The services are Spark and Python based. Other Python frameworks will work, and you can get a little hacky to call Java/Scala from Python. Not really sure what you mean by an "Azure ML Service", perhaps you are referring to the operationalization service I mentioned above. This will quickly let you create new Python based APIs using Docker containers, and will connect with the model management account to keep track of the linage between your models and your services. All services here are still in preview and may breaking change before GA release.
Azure ML Studio是一种较旧的产品，对于某些人（我自己是工程师而不是数据科学家）来说可能更简单。它提供拖放体验，但数据大小限制为大约10G。该产品是GA。
Azure ML Studio is an older product that is perhaps simpler for some(myself an engineer not a data scientist). It offers a drag and drop experience, but is limited in it's data size to about 10G. This product is GA.
是的，但是您需要较小的数据量，并且工作流程不是基于Spark的。我用它来进行快速PoC。此外，与PaaS相比，与较新的服务即IaaS相比，您将更少地控制评分的可伸缩性（批处理或实时）。对于大多数用例，我建议您查看新服务而不是Studio。
It is, but you need smaller data sizes, and the job flow is not spark based. I use this to do rapid PoC's. Also you will less control over the scalability of your scoring (batch or real time), because it is PaaS, compared to the newer service which is more IaaS. I would recommend looking at the new service instead of studio for most use cases.
Web服务完全基于Docker。需要docker进行试验的更多内容是在本地运行事情，而我本人很少这样做。但是，对于实时服务，您打包的所有内容都放置在docker容器中，因此可以将其部署到ACS群集中。
The web services are completely based on Docker. Needing docker for experimentation is more about running things locally, which I myself rarely do. But, for the real time service, everything you package is placed into a docker container so it can be deployed to an ACS cluster.
这篇关于Azure ML和Azure ML实验之间的区别的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！