Azure Machine Learning Studio is an interactive programming tool for machine learning analysis. This is the solution offered by Microsoft as a tool to create predictive models automatically without the need to know how the algorithm works. It is a platform in which data, cloud-based tools, and predictive analysis are combined to implement an effective model. The platform also has numerous APIs to help developers build advanced artificial intelligence models. Azure Machine Learning Studio is available at the following link: https://studio.azureml.net/.

The following screenshot shows the welcome page of Azure Machine Learning Studio:

In Azure Machine Learning Studio, building a development model becomes an interactive experience. It's more fun and much easier than the classic methodologies, which involve the implementation/adaptation of algorithms already available in libraries. In fact, thanks to the drag-and-drop function, it will be enough to select the necessary resources and the rest will be provided by Microsoft. Editing, saving a copy, running, converting, and publishing an experiment will be just as simple. All this is due to the visual connections available in the development environment, which does not require any programming experience, allowing in this way to develop models in a simple and fast manner.

In a time of shared experiences, a sharing place could not be missing even from Azure Machine Learning Studio. This is the Cortana Intelligence Gallery, where users can share and discover solutions among developers. The community also offers the possibility to interact with other developers in order to arrive at a shared solution of a specific problem.

Work sharing is not limited to the community offered by the Cortana Intelligence Gallery; in fact models designed in Azure Machine Learning Studio can be transferred directly to the web to get the best visibility possible. In this way, users can use our models and run them with their data. All the changes that will be made to the model will be updated in real time on the version available on the web.