An LLM-orchestrated multi-agent framework for end-to-end BDaaS automation with drift awareness is proposed and evaluated on tabular benchmarks for improved lifecycle reliability over baselines.
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Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization
An LLM-orchestrated multi-agent framework for end-to-end BDaaS automation with drift awareness is proposed and evaluated on tabular benchmarks for improved lifecycle reliability over baselines.