Maria Montero

Microsoft launches drag-and-drop machine learning tool …

Microsoft Three new services were announced today that aim to simplify the machine learning process. These range from a new interface for a tool that fully automates the model creation process, to a new no-code visual interface for building, training and deploying models, to Jupyter-style laptops for advanced users.

Getting started with machine learning is difficult. Even running the most basic experiments requires a fair amount of experience. All of these new tools simplify this process by hiding code or giving those who want to write their own code a preconfigured platform to do so.

The new interface for the Azure machine learning tool makes creating a model as easy as importing a dataset and then telling the service what value to predict. Users don’t need to write a single line of code, while on the backend, this updated version now supports a number of new algorithms and optimizations that should result in more accurate models. Although most of this is automated, Microsoft emphasizes that the service provides “full transparency in the algorithms, so that developers and data scientists can manually override and control the process.”

For those who want a little more control up front, Microsoft also today released a visual interface for its Azure Machine Learning service in preview that will allow developers to create, train, and deploy machine learning models without having to touch any code. .

This tool, the visual interface of Azure Machine Learning looks suspiciously like that of Azure ML Studio, Microsoft’s first attempt to create a visual machine learning tool. In fact, the two services appear identical. However, the company never really pushed this service, and it almost seemed to have forgotten about it despite the fact that it always seemed like a really useful tool to get started with machine learning.

Microsoft says that this new version combines the best of Azure ML Studio with the Azure Machine Learning service. In practice, this means that while the interface is nearly identical, the Azure Machine Learning visual interface extends what was possible with ML Studio by running on top of the Azure Machine Learning service and adding the security, deployment, and management capabilities of the life cycle of those services.

The service provides an easy interface to clean your data, train models with the help of different algorithms, evaluate them, and finally put them into production.

While these first two services are clearly aimed at beginners, the new notebooks hosted on Azure Machine Learning are clearly geared toward the more experienced machine learning professional. The laptops come prepackaged with support for the Azure Machine Learning Python SDK and run in what the company describes as a “secure and enterprise-ready environment.” Although the use of these laptops is not trivial, this new feature allows developers to quickly get started without the hassle of setting up a new development environment with all the necessary cloud resources.