As enterprises increasingly adopt Kubernetes for its scalability and self-managing capabilities, traditional monitoring approaches must adapt to address the complexities of this dynamic and distributed system. Kubernetes presents challenges due to its ephemeral nature and the extensive volume of data generated by containerized microservices across numerous pods, necessitating new strategies for effective observability. This includes the potential use of machine learning to correlate disparate signals and provide actionable insights. Additionally, the rise of managed Kubernetes services from major cloud providers offers varying levels of management and access, which influence monitoring needs. Tools like Elastic Observability aim to address these challenges by providing integrated solutions that consolidate telemetry data, offer customizable dashboards, and enable automated alerts, thereby facilitating better Kubernetes monitoring without requiring deep expertise.