AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An AI-augmented approach to data quality monitoring within real-time data pipelines on AWS is proposed that integrates machine learning models for anomaly detection, data drift analysis, and predictive quality assessment into the AWS streaming ecosystem.
This research proposes an AI-driven resource scheduling framework that integrates workload prediction, resource classification, intelligent scheduling, and continuous feedback mechanisms that aims to optimize multiple objectives, including cost reduction, execution efficiency, energy consumption, and SLA compliance.
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