Can Automated ML infer training data from the use case provided?

Automated Machine Learning (AutoML) tools are designed to streamline the machine learning process, making it more accessible for users without deep expertise in data science. However, these systems require a defined dataset to train models effectively. Automated ML does not infer or generate training data based solely on a given use case; instead, it relies on existing data provided by the user to learn patterns and make predictions.

The use case may help guide the selection of algorithms and approaches, but it does not substitute for the necessity of having adequate and relevant training data. Therefore, stating that Automated ML cannot infer training data from the use case provided accurately reflects its operational parameters.

A deeper understanding of what Automated ML offers highlights its reliance on existing datasets rather than generating new data from the use case, making 'No' the right selection.