End to End AutoML

End-to-end AutoML aims to automate the entire machine learning pipeline, from data preprocessing to model selection and hyperparameter tuning, minimizing human intervention. Current research emphasizes improving efficiency and scalability through techniques like hardware-aware ensemble selection, grammar-guided genetic programming, and scalable search space decomposition, often incorporating elements of reinforcement learning and meta-learning. This field is significant because it democratizes machine learning by making it accessible to non-experts and accelerates the development process for experts, leading to faster deployment of more effective models across diverse applications.