Efficient resource management in Kubernetes is key to balancing performance and costs. This session will explore how AI algorithms can predict and optimize resource allocation for Kubernetes workloads, ensuring clusters are right-sized without over-provisioning. We’ll dive into techniques like predictive autoscaling, where AI anticipates workload fluctuations and adjusts resources proactively, and cost-efficient provisioning, optimizing CPU, memory, and storage usage. We’ll also cover memory optimization for AI applications, improving cluster efficiency. By integrating AI, organizations can achieve better cost control and scalable performance, making Kubernetes clusters both efficient and cost-effective. The session will provide practical strategies and real-world examples for using AI to automate Kubernetes resource management, ensuring optimal performance while minimizing unnecessary resource consumption.