Difference between Azure ML and Azure ML experimentation

Question

I am new to Azure ML. I am having some doubts .Could anyone please clarify my doubts listed below.

1. What is the difference between Azure ML service Azure ML exp

Answer 1

I'll do my best to answer these questions and feel free to ask more questions. :)

What is the difference between Azure ML service Azure ML experimentation service?

Essentially, Azure ML Service (I may reference this as Azure ML Studio) uses a drag and drop interface to build out your workflow and test models. Azure ML experimentation is a new offering from the Azure Portal to host them directly in Azure and offer a better way to manage your models. Experimentation will use Azure ML Workbench to build out your models.

What is the difference between Azure ML workbench and Azure ML Studio?

The biggest difference is ML Studio has the drag and drop interface to build the workflow and models, whereas Workbench lets you use Python to programmatically build out your models. Workbench also includes a really nice and powerful way to clean your data from the app. In Studio you have some good modules to clean data, but I don't think it's as powerful as what you can do in Workbench.

EDIT: The Workbench application is deprecated and has been replaced by/upgraded to ML Services. The core functionality is unchanged, though.

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?

I would actually say it's much easier to do this in ML Studio. The drag and drop interface is very intuitive and it is only a couple of clicks to create a web API to call your model. I feel, as it is currently at the time of this writing, is more complex to deploy your model and it involves using the Azure CLI.

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?

Here I'm not too familiar with the Docker parts of Workbench, but I believe you can create and deploy without using Docker. It will require an Azure Model Management resource, though, I believe.

I hope this helps and, again, feel free to ask more questions.