Streaming to Predictive: Automating Cloud AI Workflows in Transaction-Intensive Digital Ecosystems

The fast pace of transformation of systems heavy on transactions, such as the digital, has created the need to intelligently, yet at scale, represent, process and act upon huge amounts of information in real time. The proposed framework discussed in this paper switches the traditional streaming architecture into a predictive, automated cloud AI workflow. The proposed model simultaneously provides a decision-making improvement in high-throughput applications like finance, retail and smart infrastructure by using edge-to-cloud orchestration, real-time data pipelines and event processing through AI. The architecture connects the stream ingestion, the feature extraction, the federated learning and the predictive analytics into a unified pipeline with a minimum amount of manual interaction. There is a more significant latency management, more precise prediction, and resiliency in performing different loads of transactions. This piece highlights how cloud-native AI automation has the potential to change reactive streaming backends into proactive intelligent platforms, which can intelligently react and scale in dynamically changing digital environments.