What is the main function of AWS SageMaker Notebooks?

AWS SageMaker Notebooks serve as an interactive environment specifically designed for machine learning practitioners and data scientists. This environment enables users to explore data, perform data analysis, and develop machine learning models in a cohesive and streamlined manner. The interactive nature of SageMaker Notebooks allows users to write code in languages such as Python, R, or Julia, and execute it in real-time, facilitating immediate feedback and fostering an efficient workflow for experimentation and model development.

Moreover, SageMaker Notebooks come pre-configured with popular machine learning libraries and frameworks, which further streamlines the process of building and training models. These notebooks support a range of functionalities, including data preprocessing, model training, and hyperparameter tuning, making them a comprehensive tool for both beginners and experienced practitioners in the machine learning domain.

In contrast, other options focus on functionalities that are not the primary purpose of SageMaker Notebooks. For example, providing a storage solution or enhancing data visualization are capabilities that can be achieved with other AWS services, such as S3 for storage and QuickSight for visualization, but they do not represent the core function of SageMaker Notebooks. Automated data labeling, while a part of the broader SageMaker suite, is not the main focus of the notebooks themselves. Thus