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
Researchers have developed a new framework for Big-Data-as-a-Service (BDaaS) that utilizes a multi-agent system orchestrated by a central LLM. This system aims to automate and improve the reliability of the entire data engineering and MLOps lifecycle, from ingestion to post-deployment monitoring and drift detection. The proposed architecture decomposes tasks into specialized agents, enhancing lifecycle-level orchestration, artifact governance, and human oversight compared to existing single-agent or AutoML-only approaches. AI
IMPACT This framework could streamline and improve the reliability of complex data pipelines, potentially accelerating the deployment and maintenance of AI models in production environments.