Abstract
The presentation outlines the evolution of frameworks for end-to-end workflow automation, designed to address the complexities of building, deploying, and maintaining robust edge AI autonomous systems. It explores the integrated approach that automates the entire lifecycle, from data ingestion and model training to deployment on heterogeneous edge devices and continuous operational monitoring.