Scalable Data Pipelines in Cloud Computing: Optimizing AI Workflows for Real-Time Processing

Abstract

Scalable data pipelines in cloud computing are crucial for optimizing AI workflows, particularly for real-time data processing in dynamic environments. This paper explores strategies for constructing resilient, efficient, and scalable data pipelines that seamlessly integrate with cloud infrastructure to support artificial intelligence (AI) and machine learning (ML) models. Leveraging distributed systems, containerization, and serverless architectures, the proposed approach enhances data ingestion, transformation, and storage while ensuring low latency and high throughput. Furthermore, adaptive resource management techniques are employed to dynamically adjust computing power, reducing operational costs and improving performance. By addressing challenges such as data bottlenecks, processing delays, and scalability limits, this study offers a blueprint for building robust pipelines that accelerate AI decision-making processes in timesensitive applications like predictive maintenance, fraud detection, and autonomous systems. The paper concludes with recommendations for future advancements in real-time AI processing via cloud-optimized pipelines.