Kubernetes optimization
Survey: Few IT Teams Can Continuously Optimize Kubernetes Clusters
A survey of 321 Kubernetes practitioners at organizations with more than 1,000 employees published today finds that while 89% recognize that automation is crucial, only 17% are able to continuously optimize the ...
Komodor Extends Autonomous AI Agent for Optimizing Kubernetes Clusters
Komodor today added autonomous self-healing and cost optimization capabilities to an artificial intelligence (AI) platform designed to automate site reliability engineering (SRE) workflows across Kubernetes environments. Company CTO Itiel Shwartz said those ...
Machine Learning in Kubernetes: Why Trust, Not Tech, is Your Biggest HurdleÂ
Explore why trust—not technology—is the real barrier to ML-driven Kubernetes optimization and how intelligent automation builds confidence at scale ...
Yasmin Rajabi | | AI in DevOps, AI in infrastructure management, AI-driven automation, automated cloud governance, cloud cost optimization, cloud efficiency, container optimization, continuous optimization, developer trust, devops, FinOps, intelligent automation, KubeCon 2025, Kubernetes optimization, Kubernetes performance, Kubernetes resource management, Kubernetes trust gap, machine learning in Kubernetes, ML in cloud infrastructure, ML-based cost control, ML-powered rightsizing, platform engineering, platform reliability, predictive scaling

