Why Your Kubernetes Ship Is Sunk without Machine Learning - The New Stack

With the rise of containerized services based on service-oriented architecture (SOA), the need for orchestration software like Kubernetes is rapidly increasing. Kubernetes is ideally suited for large-scale systems, but its complexity and lack of transparency can result in increased cloud costs, deployment delays and frustration among stakeholders. Used by large enterprises to scale their applications and underlying infrastructure vertically and horizontally to meet varied loads, the fine-grained control that makes Kubernetes so adaptable also makes it challenging to tune and optimize effectively. The Kubernetes architecture makes autonomous workload allocation decisions within a cluster. However, Kubernetes in itself doesn't ensure high availability. It will easily operate in a production environment with only one primary node.