Cloud Computing Frameworks for Real-Time AI and ML Data Processing
Keywords:
Real-Time Data Processing, Cloud Computing Frameworks, Artificial Intelligence, Machine Learning, Apache Kafka, Apache Flink.Abstract
Real-time data processing has become a cornerstone for AI and machine learning (ML) applications in cloud computing, necessitating robust frameworks that can efficiently handle highvelocity and high-volume data streams. This paper explores state-of-the-art cloud computing frameworks tailored for real-time AI and ML data processing. We analyze various cloud-native platforms, including Apache Kafka, Apache Flink, and Google Cloud Dataflow, to assess their capabilities in managing real-time data ingestion, processing, and analytics. Additionally, we integrate AI models like Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Reinforcement Learning (RL) algorithms into these frameworks to evaluate their performance in terms of latency, scalability, and resource optimization. The study presents a comparative analysis of these frameworks based on key metrics such as processing throughput, fault tolerance, and cost efficiency. By deploying these AI models in different cloud environments, we demonstrate how real-time data processing can be optimized to enhance decision-making, predictive analytics, and automated responses. The findings provide valuable insights into selecting and implementing cloud computing frameworks that align with the dynamic requirements of AI and ML applications, paving the way for more efficient, responsive, and intelligent cloud infrastructures.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.