Which feature of Amazon SageMaker allows for automatic training and tuning of machine learning models?

Amazon SageMaker Autopilot is specifically designed to automate the process of preparing data, training, and tuning machine learning models. It streamlines the machine learning workflow by allowing users to simply provide their dataset, and Autopilot takes care of feature engineering, model selection, and hyperparameter optimization automatically.

This feature is particularly beneficial for users who may not have deep expertise in machine learning or those who want to accelerate the model development process without manually tweaking parameters or testing different algorithms. Autopilot generates a series of model candidates and selects the best-performing one based on the evaluation metric specified by the user.

In contrast, Amazon SageMaker Ground Truth is focused on facilitating the creation of labeled datasets by managing the labeling process, leveraging human labelers, and supporting active learning. SageMaker Studio provides a unified interface for managing ML workflows but doesn't specifically automate model training or tuning. Lastly, SageMaker Notebooks offer a managed Jupyter notebook environment for coding and experimentation, but again, they do not provide the same level of automation for model training and hyperparameter tuning as Autopilot does.