Future AutoML

Future AutoML research is shifting towards a more human-centered approach, prioritizing user interaction and trust in automated machine learning systems. Current efforts focus on improving transparency and explainability, addressing the high computational cost of AutoML, and integrating AutoML with continual learning paradigms to enhance adaptability to evolving data streams. This research is crucial for broadening AutoML's accessibility, fostering wider adoption, and mitigating its environmental impact, ultimately leading to more responsible and efficient machine learning practices.