What method allows users to create and train models quickly with minimal technical effort?

The correct choice is AutoML, which is designed specifically to simplify the process of model creation and training. AutoML automates traditional machine learning tasks such as data preprocessing, model selection, and hyperparameter optimization, enabling users—especially those who may not have an extensive technical background—to generate high-quality models with minimal manual intervention.

This approach is beneficial because it significantly reduces the time and expertise required to develop machine learning models. Users can simply provide their data and the desired outcome, and AutoML takes care of the underlying complexities. It selects the best algorithms and processes that align with the specific characteristics of the data, streamlining the entire workflow.

In contrast, model tuning and hyperparameter adjustment typically require a deeper understanding of the model's architecture and the machine learning process. These methods involve a more hands-on approach, which may not be suitable for users looking to achieve results quickly and easily. Transfer learning, while also powerful, usually necessitates some expertise with existing models and a certain understanding of how to adapt them to new tasks, which can be more complex than the AutoML approach.