Streamlining Model Selection and Hyperparameter Tuning in Cloud-based AI: A Comprehensive Review

Abstract

The advent of cloud computing has revolutionized the landscape of artificial intelligence (AI), enabling researchers and practitioners to harness vast computational resources for model development and deployment. Automated model selection and hyperparameter tuning have emerged as pivotal techniques in enhancing the efficiency and efficacy of AI systems. This research paper provides a concise overview of automated model selection and hyperparameter tuning techniques in cloud-based AI, discussing their significance, challenges, and recent advancements. Additionally, it explores key methodologies, frameworks, and tools utilized in this domain, along with potential future directions.