Dynamically Tuning Pods - Leveraging Time Series ML Models with KubeFlow

Explore an advanced solution for dynamically tuning pod resource constraints in Kubernetes clusters using time series machine learning models with KubeFlow. Learn how to integrate Prometheus and KubeFlow to address the challenge of determining optimal resource constraints for pods in complex and large-scale Kubernetes environments. Discover a method that leverages Prometheus' telemetry and KubeFlow's predictive capabilities to anticipate workload demands and improve cluster efficiency. Watch a comprehensive demonstration of the end-to-end pipeline, covering data collection, model training, and real-time application of constraints within Kubernetes. Gain insights into enhancing Kubernetes deployment performance and efficiency through this innovative approach to resource management.

Dynamically Tuning Pods: Leveraging Time Series ML Models with KubeFlow - Christopher Nuland