What is a key feature of Azure Machine Learning Studio?

A key feature of Azure Machine Learning Studio is its drag-and-drop interface for building models. This intuitive visual interface allows data scientists, even those who may not have extensive coding experience, to easily create machine learning workflows. Users can seamlessly connect different components of a machine learning pipeline, such as data input, data preprocessing, model training, and evaluation, by simply dragging and dropping them onto the canvas.

This feature significantly enhances productivity and lowers the barrier to entry for machine learning development, making it accessible to a broader audience. The ease of use provided by the drag-and-drop functionality promotes experimentation and adaptation, allowing practitioners to focus on refining their models and approaches rather than being bogged down by complex coding.

The other options, while they relate to various aspects of Azure services, do not specifically highlight the core strengths of Azure Machine Learning Studio in model building. Data warehousing, stream processing capabilities, and CDN integration serve different purposes within the Azure ecosystem and do not directly pertain to the model-building process within Azure Machine Learning Studio.