Automated Intelligence: Designing Next-Gen AI Architectures with AutoML
Murali Krishna Pasupuleti · 2024
Abstract: The chapter "Automated Intelligence: Designing Next-Gen AI Architectures with AutoML" explores the transformative potential of Automated Machine Learning (AutoML) in revolutionizing AI development. It delves into the core components of AutoML, such as data preprocessing, model selection, and hyperparameter optimization, and highlights the technologies driving this innovation, including Neural Architecture Search (NAS), meta-learning, and reinforcement learning. Practical applications across industries, from healthcare to finance, are examined to illustrate AutoML's impact on real-world problems. The chapter also addresses the challenges and limitations, such as data quality, interpretability, and computational resource constraints. Finally, it discusses future directions, including emerging trends and the ethical and societal implications of AutoML, emphasizing the importance of responsible adoption and continuous advancement in this field. Keywords: AutoML, Automated Machine Learning, AI architectures, Neural Architecture Search, meta-learning, reinforcement learning, data preprocessing, model selection, hyperparameter optimization, AI democratization, explainable AI, computational resources, ethical AI, future trends, AI in industry.