Optimizing Cloud-Based Data Pipelines with Machine Learning and AI
Keywords:
Cloud Computing, Data Pipelines, Machine Learning, Artificial Intelligence, Data Optimization.Abstract
The rapid evolution of cloud computing has revolutionized data management and analytics, necessitating more sophisticated approaches for optimizing data pipelines. This paper explores the integration of Machine Learning (ML) and Artificial Intelligence (AI) techniques to enhance the efficiency and effectiveness of cloud-based data pipelines. By leveraging advanced algorithms and intelligent systems, organizations can significantly improve data processing, storage, and retrieval operations, leading to optimized performance and cost reductions. This study evaluates various ML and AI methods applied to cloud data pipelines, including predictive analytics, anomaly detection, and automated resource management. The effectiveness of these methods is assessed through empirical experiments and case studies across different industries, highlighting improvements in processing speed, data quality, and operational costs. The results demonstrate that the adoption of AI-driven optimization strategies can lead to substantial gains in pipeline efficiency and scalability, offering valuable insights for organizations seeking to maximize the potential of their cloud-based data infrastructures.