Unpacking AutoML: Myths and Realities

This chapter addresses common misconceptions about AutoML, clarifying that it requires good data and understanding for effective use. It compares perspectives from academia and industry, emphasizing the need for collaboration to enhance machine learning tools. The discussion also explores the future potential of AutoML, its integration with large language models, and its role in improving efficiency while addressing the importance of domain knowledge.