Investigating the Application of Transfer Learning Techniques in Cloud-Based AI Systems for Improved Performance and Reduced Training Time

Abstract

This current research paper examines the adaptive technology solution approaches of transfer learning in a cloud environment for AI systems’ enhanced results and faster training periods. Concerning transfer learning methods, their application with cloud computing environments, and their effects on the efficiency of the AI model are the subject of the study. In this work, after reviewing the current literature and the state of the art of transfer learning and cloud-based AI, we discuss their integration’s prospects and opportunities for scalability, data privacy, and model generalization. The study sheds light on how transfer learning can go a long way in strengthening the efficiency of cloud AI, especially in facets such as speech and language processing, image identification, and speech recognition. The results of our study point out that it is possible to significantly improve the efficiency of model training and accuracy by applying transfer learning methodologies, thus opening opportunities for more dynamic AI solutions in the cloud context.

Keywords

• Transfer Learning, Cloud Computing, Artificial Intelligence, Machine Learning, Distributed Computing, Model Optimization, Fine-tuning, Domain Adaptation, Federated Learning, Performance Metrics