Many Machine Learning solutions have the same steps in common. For example, you will need to retrieve data from one or more sources; you will want to split your data into training and testing subsets; and you will want to clean up your source data. I refer to these as "plumbing tasks” because they are common to so many projects; and spending time coding these tasks takes time away from working on your data and your solution.

Azure Machine Learning Studio can help.

Azure Machine Learning Studio or "ML Studio" is a graphical design tool for building machine learning solutions. It includes a design surface and a set of shapes to perform specific tasks.

To work with ML Studio, drag a shape onto the design surface, set some properties, and connect the inputs and/or outputs to other shapes to build the workflow of your solution. For example, you can drag an "Import Data" shape onto a form and set information about the data source and data type. This is "plumbing" code that you do not have to write.

If a shape for a desired task does not exist, there are shapes that allow you to write custom code. Supported languages are Python and R.

When you finish building and testing your solution, buttons at the bottom allow you to configure and deploy a web service, so that your model is accessible via a simple API. There is even a test page, allowing you to call this API from within your browser.

You can get a free trial at https://studio.azureml.net/

There are limits to the free version. You cannot configure the size and number of instances on which it will run, and you are limited to 10 GB storage. If you cannot work within these restrictions, you can sign up for an Azure account and pay for the resources you use. Current pricing is available at this link.

If you are looking for a quick and simple way to build a machine learning solution, Azure Machine Learning Studio may be the tool for you.