Combined Cloud and Deep Learning Models for Enhanced Decision Support

Cloud computing and deep learning are changing decision-support systems. This combination was driven by cloud computing and deep learning models’ complimentary data processing capabilities. Traditional decision-support systems typically fail due to the complexity and variety of current information, particularly unstructured and semi-structured data. Innovative methods are required to overcome this restriction and exploit big data’s immense potential. Cloud computing is essential for handling and storing huge amounts of data because to its design, scalability, and storage capacity. Company data is securely accessed, processed, and stored while being cost-effective and flexible. Deep learning models—a subset of AI—offer a great toolbox of neural networks and algorithms for analyzing complex data patterns. These models are versatile, train well, and can find patterns in unstructured data. However, ideal outcomes demand a lot of processing power. Deep learning and cloud computing operate together better than traditional decision-support systems. Instead of static, rule-based models, the decision-support system may now react to complicated, ever-changing data. Modern cloud-based algorithms enable organizations to make accurate, personalized choices in real time. These changes will affect healthcare, finance, manufacturing, and autonomous automobiles. Convergence causes actual concerns. Data security, privacy, and equality are key concerns. Cloud systems are difficult to balance between usability and safety. Since deep learning algorithms may be prejudiced and provide unfair outcomes, we must continue to promote fairness and transparency. Additional challenges to the effective implementation of these converged systems include controlling scalability, ensuring compliance with laws, and addressing interoperability.