Self-Adaptive Artificial Intelligence Systems for Large-Scale Data-Driven Decision Making

The growing magnitude, diversity, and speed of data produced across contemporary cyber-physical, enterprise, and cloud-edge systems has revealed inherent constraints with the inflexible artificial intelligence (AI) models regarding providing real-time choices. Self-adaptive artificial intelligence systems have become an attractive paradigm to overcome these challenges through enabling continuous learning, dynamic model reconfiguration and context-specific optimization in the changing data distributions and operational constraints. In this paper, a detailed self-adaptive AI architecture to serve large-scale decision making with data is provided, which involves online learning, feedback-based adjustment, and automatic policy optimization. The suggested architecture takes advantage of the hybrid intelligence methods merging deep learning, reinforcement learning, and meta-learning to reach strong performance in non-stationary and uncertain environments. Adaptive control loops are incorporated to check data drift, system performance and the consumption of resources and recalibration of a model can be done in real time without involving human intervention. The comprehensive test on large-scale datasets indicates that the proposed solution is much more accurate in making decisions, flexible and resilient to systems against the traditional non-adaptable and semi-adaptive AI models. The outcomes confirm the effectiveness of self-adaptive intelligence in aiding scalable, dependable, and explainable decision-making of dynamic data-intensive applications including smart infrastructure, autonomous systems, and intelligent business analytics.