(untitled) (International Journal of Engineering and Computer Science, 2021)
Artificial intelligence (AI) workflows are revolutionizing industries, enabling advancements in automation, data analysis, and decision-making. However, deploying AI applications poses significant challenges, including resource-intensive computation, latency issues, data security concerns, and the complexity of integrating diverse components. Addressing these challenges necessitates innovative approaches that balance computational efficiency with practical scalability. This study explores the integration of cloud computing and edge devices as a transformative solution for optimizing AI workflows. Cloud computing offers immense processing power and centralized data storage, enabling robust model training and large-scale analytics. In contrast, edge devices provide localized computation, reducing latency and ensuring real-time decision-making closer to the data source. Combining these technologies creates a hybrid model that leverages the strengths of both paradigms. The methodology involves designing a hybrid AI workflow that offloads training tasks to cloud resources while deploying lightweight inference models to edge devices. Key findings demonstrate that this approach significantly reduces data transfer requirements, improves response times by up to 40%, and ensures enhanced data privacy by processing sensitive information locally. Real-world applications, such as smart healthcare and industrial IoT, validate the efficacy of this synergy. In conclusion, the combination of cloud computing and edge devices represents a pivotal advancement in AI workflows. This synergy not only addresses deployment challenges but also enhances the scalability, efficiency, and reliability of AI applications, paving the way for more accessible and impactful technological solutions.