Autonomous Optimization for Kubernetes Applications and Clusters

Emphasizing the importance of right-sizing workloads and infrastructure. They highlight the complexities of auto-scaling, introducing Sedai's autonomous approach, which leverages machine learning for continuous optimization, predictive scaling, and efficient node selection, ultimately improving cost efficiency and performance in Kubernetes clusters.