Comparative Analysis of AutoML and Traditional Model Development for Enterprise AI Solutions
Abstract
Automated machine learning (AutoML) platforms have simplified AI development by reducing the need for manual model selection and hyperparameter tuning. This paper presents a comparative study of AutoML frameworks, such as Google AutoML and H2O.ai, against traditional model development approaches in enterprise AI applications. We assess their performance, scalability, and ease of use across industries such as finance, healthcare, and e-commerce. The findings provide valuable insights into the trade-offs between automation and expert-driven AI model optimization.
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