Autonomous Cloud-Native Analytics Pipelines Powered by Generative AI and AIOps for Intelligent Workflow Orchestration

Abstract

Cloud-native industry analytics pipelines distilling insights from data-to-decision in a fully-automated manner promise to reduce the ever-growing burden of repetitive, manual operations on data engineering and analytics teams. Managing the supporting infrastructure and flow from data ingestion to decision-making undertaken by the pipeline needs to be completely automated; in essence a self-driving data-to-decision pipeline. Doing so calls for the blending of Generative AI and AIOps for end-to-end workflow automation, where AI technologies assume the entire range of operational responsibilities—monitoring infrastructure, tracking user behavior dynamics, spotting anomalies, triggering corrective/fine-tuning actions, and ensuring safety during execution—while needing to be reinforced through human oversight and support. A conceptual framework is established outlining the synergy of Generative AI and AIOps for autonomous pipeline operation with key research opportunities highlighted. As with any conceptual framework, the presented blend of the two paradigms must ultimately be demonstrated in practice for a specific use case to evaluate its effectiveness. Cloud-native Industry Analytics Pipelines are an ideal choice; spanning the full spectrum of Generative AI, including data ingestion and preparation tasks that are ripe for automation, such a pipeline delivers actionable insights—key opportunities, risks, predictions, and recommendations—on a live basis to a network of stakeholders at all levels. The choice of a cloud-native environment further facilitates the use of AIOps, with observability and control directly built in, enabling seamless extension of monitoring and automation capabilities for the autonomous operation of the pipeline—the desired self-driving Dynamics.